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+ + Computation and Language 70 +
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+ + ☆ Distributed Mixture-of-Agents for Edge Inference with Large Language + Models +
+ +
+ Mixture-of-Agents (MoA) has recently been proposed as a method to enhance
+performance of large language models (LLMs), enabling multiple individual LLMs
+to work together for collaborative inference. This collaborative approach
+results in improved responses to user prompts compared to relying on a single
+LLM. In this paper, we consider such an MoA architecture in a distributed
+setting, where LLMs operate on individual edge devices, each uniquely
+associated with a user and equipped with its own distributed computing power.
+These devices exchange information using decentralized gossip algorithms,
+allowing different device nodes to talk without the supervision of a
+centralized server. In the considered setup, different users have their own LLM
+models to address user prompts. Additionally, the devices gossip either their
+own user-specific prompts or augmented prompts to generate more refined answers
+to certain queries. User prompts are temporarily stored in the device queues
+when their corresponding LLMs are busy. Given the memory limitations of edge
+devices, it is crucial to ensure that the average queue sizes in the system
+remain bounded. In this paper, we address this by theoretically calculating the
+queuing stability conditions for the device queues under reasonable
+assumptions, which we validate experimentally as well. Further, we demonstrate
+through experiments, leveraging open-source LLMs for the implementation of
+distributed MoA, that certain MoA configurations produce higher-quality
+responses compared to others, as evaluated on AlpacaEval 2.0 benchmark. The
+implementation is available at:
+https://github.com/purbeshmitra/distributed_moa.
+
+
+
+
+ + ☆ HumanEval Pro and MBPP Pro: Evaluating Large Language Models on + Self-invoking Code Generation +
+ +
+ We introduce self-invoking code generation, a new task designed to evaluate
+the progressive reasoning and problem-solving capabilities of LLMs. In this
+task, models are presented with a base problem and a related, more complex
+problem. They must solve the base problem and then utilize its solution to
+address the more complex one. This work features three key contributions.
+First, we propose a general recipe for generating more challenging versions of
+existing benchmarks, resulting in three new benchmarks: HumanEval Pro, MBPP
+Pro, and BigCodeBench-Lite Pro, specifically designed to assess LLMs on
+self-invoking code generation. Second, from the analysis of experimental
+results over twenty LLMs on our benchmarks, we have two important observations:
+(i) Most LLMs excel in traditional code generation benchmarks like HumanEval
+and MBPP, but their performance declines on self-invoking tasks. For example,
+o1-mini achieves 96.2% pass@1 on HumanEval but only 76.2% on HumanEval Pro.
+(ii) On self-invoking code generation task, the instruction-tuned models
+demonstrate only marginal improvements compared to the base models. Third, we
+disclose the types of failure modes that exist in our evaluation results. All
+these results underscore the need for further advancements in self-invoking
+code generation tasks and provide a new direction for future research on
+enhancing LLMs' code reasoning capabilities.
+
+
+
+
+ + ☆ Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs +
+
+
+
+
+
+
+
+ Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu
+
+
+ The remarkable performance of models like the OpenAI o1 can be attributed to
+their ability to emulate human-like long-time thinking during inference. These
+models employ extended chain-of-thought (CoT) processes, exploring multiple
+strategies to enhance problem-solving capabilities. However, a critical
+question remains: How to intelligently and efficiently scale computational
+resources during testing. This paper presents the first comprehensive study on
+the prevalent issue of overthinking in these models, where excessive
+computational resources are allocated for simple problems with minimal benefit.
+We introduce novel efficiency metrics from both outcome and process
+perspectives to evaluate the rational use of computational resources by o1-like
+models. Using a self-training paradigm, we propose strategies to mitigate
+overthinking, streamlining reasoning processes without compromising accuracy.
+Experimental results show that our approach successfully reduces computational
+overhead while preserving model performance across a range of testsets with
+varying difficulty levels, such as GSM8K, MATH500, GPQA, and AIME.
+
+
+
+ comment: Work in progress
+
+
+
+ + ☆ Two-component spatiotemporal template for activation-inhibition of + speech in ECoG +
+ +
+ I compute the average trial-by-trial power of band-limited speech activity
+across epochs of multi-channel high-density electrocorticography (ECoG)
+recorded from multiple subjects during a consonant-vowel speaking task. I show
+that previously seen anti-correlations of average beta frequency activity
+(12-35 Hz) to high-frequency gamma activity (70-140 Hz) during speech movement
+are observable between individual ECoG channels in the sensorimotor cortex
+(SMC). With this I fit a variance-based model using principal component
+analysis to the band-powers of individual channels of session-averaged ECoG
+data in the SMC and project SMC channels onto their lower-dimensional principal
+components.
+ Spatiotemporal relationships between speech-related activity and principal
+components are identified by correlating the principal components of both
+frequency bands to individual ECoG channels over time using windowed
+correlation. Correlations of principal component areas to sensorimotor areas
+reveal a distinct two-component activation-inhibition-like representation for
+speech that resembles distinct local sensorimotor areas recently shown to have
+complex interplay in whole-body motor control, inhibition, and posture. Notably
+the third principal component shows insignificant correlations across all
+subjects, suggesting two components of ECoG are sufficient to represent SMC
+activity during speech movement.
+
+
+
+
+ + ☆ Aviary: training language agents on challenging scientific tasks +
+
+
+
+
+
+
+
+ Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, Manu Ponnapati, Albert Bou, Jon Laurent, Ori Kabeli, Geemi Wellawatte, Sam Cox, Samuel G. Rodriques, Andrew D. White
+
+
+ Solving complex real-world tasks requires cycles of actions and observations.
+This is particularly true in science, where tasks require many cycles of
+analysis, tool use, and experimentation. Language agents are promising for
+automating intellectual tasks in science because they can interact with tools
+via natural language or code. Yet their flexibility creates conceptual and
+practical challenges for software implementations, since agents may comprise
+non-standard components such as internal reasoning, planning, tool usage, as
+well as the inherent stochasticity of temperature-sampled language models.
+Here, we introduce Aviary, an extensible gymnasium for language agents. We
+formalize agents as policies solving language-grounded partially observable
+Markov decision processes, which we term language decision processes. We then
+implement five environments, including three challenging scientific
+environments: (1) manipulating DNA constructs for molecular cloning, (2)
+answering research questions by accessing scientific literature, and (3)
+engineering protein stability. These environments were selected for their focus
+on multi-step reasoning and their relevance to contemporary biology research.
+Finally, with online training and scaling inference-time compute, we show that
+language agents backed by open-source, non-frontier LLMs can match and exceed
+both frontier LLM agents and human experts on multiple tasks at up to 100x
+lower inference cost.
+
+
+
+
+ + ☆ Facilitating large language model Russian adaptation with Learned + Embedding Propagation +
+ +
+ Rapid advancements of large language model (LLM) technologies led to the
+introduction of powerful open-source instruction-tuned LLMs that have the same
+text generation quality as the state-of-the-art counterparts such as GPT-4.
+While the emergence of such models accelerates the adoption of LLM technologies
+in sensitive-information environments the authors of such models don not
+disclose the training data necessary for replication of the results thus making
+the achievements model-exclusive. Since those open-source models are also
+multilingual this in turn reduces the benefits of training a language specific
+LLMs as improved inference computation efficiency becomes the only guaranteed
+advantage of such costly procedure. More cost-efficient options such as
+vocabulary extension and subsequent continued pre-training are also inhibited
+by the lack of access to high-quality instruction-tuning data since it is the
+major factor behind the resulting LLM task-solving capabilities. To address the
+limitations and cut the costs of the language adaptation pipeline we propose
+Learned Embedding Propagation (LEP). Unlike existing approaches our method has
+lower training data size requirements due to minimal impact on existing LLM
+knowledge which we reinforce using novel ad-hoc embedding propagation procedure
+that allows to skip the instruction-tuning step and instead implant the new
+language knowledge directly into any existing instruct-tuned variant. We
+evaluated four Russian vocabulary adaptations for LLaMa-3-8B and Mistral-7B,
+showing that LEP is competitive with traditional instruction-tuning methods,
+achieving performance comparable to OpenChat 3.5 and LLaMa-3-8B-Instruct, with
+further improvements via self-calibration and continued tuning enhancing
+task-solving capabilities.
+
+
+
+ comment: Preprint version of an article published in the Journal of Language
+ and Education. Copyright held by the owner/author(s). Publication rights
+ licensed to the Journal of Language and Education
+
+
+
+ + ☆ Training Software Engineering Agents and Verifiers with SWE-Gym +
+
+
+
+
+
+
+
+ Jiayi Pan, Xingyao Wang, Graham Neubig, Navdeep Jaitly, Heng Ji, Alane Suhr, Yizhe Zhang
+
+
+ We present SWE-Gym, the first environment for training real-world software
+engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task
+instances, each comprising a codebase with an executable runtime environment,
+unit tests, and a task specified in natural language. We use SWE-Gym to train
+language model based SWE agents , achieving up to 19% absolute gains in resolve
+rate on the popular SWE-Bench Verified and Lite test sets. We also experiment
+with inference-time scaling through verifiers trained on agent trajectories
+sampled from SWE-Gym. When combined with our fine-tuned SWE agents, we achieve
+32.0% and 26.0% on SWE-Bench Verified and Lite, respectively, reflecting a new
+state-of-the-art for open-weight SWE agents. To facilitate further research, we
+publicly release SWE-Gym, models, and agent trajectories.
+
+
+
+ comment: Code at https://github.com/SWE-Gym/SWE-Gym
+
+
+
+ + ☆ Exploring and Controlling Diversity in LLM-Agent Conversation AAAI 2025 +
+ +
+ Diversity is a critical aspect of multi-agent communication. In this paper,
+we focus on controlling and exploring diversity in the context of open-domain
+multi-agent conversations, particularly for world simulation applications. We
+propose Adaptive Prompt Pruning (APP), a novel method that dynamically adjusts
+the content of the utterance generation prompt to control diversity using a
+single parameter, lambda. Through extensive experiments, we show that APP
+effectively controls the output diversity across models and datasets, with
+pruning more information leading to more diverse output. We comprehensively
+analyze the relationship between prompt content and conversational diversity.
+Our findings reveal that information from all components of the prompt
+generally constrains the diversity of the output, with the Memory block
+exerting the most significant influence. APP is compatible with established
+techniques like temperature sampling and top-p sampling, providing a versatile
+tool for diversity management. To address the trade-offs of increased
+diversity, such as inconsistencies with omitted information, we incorporate a
+post-generation correction step, which effectively balances diversity
+enhancement with output consistency. Additionally, we examine how prompt
+structure, including component order and length, impacts diversity. This study
+addresses key questions surrounding diversity in multi-agent world simulation,
+offering insights into its control, influencing factors, and associated
+trade-offs. Our contributions lay the foundation for systematically engineering
+diversity in LLM-based multi-agent collaborations, advancing their
+effectiveness in real-world applications.
+
+
+
+ comment: Accepted for the AAAI 2025 Workshop on Advancing LLM-Based
+ Multi-Agent Collaboration
+
+
+
+ + ☆ Efficient Multi-Task Inferencing with a Shared Backbone and Lightweight + Task-Specific Adapters for Automatic Scoring AAAI +
+ +
+ The integration of Artificial Intelligence (AI) in education requires
+scalable and efficient frameworks that balance performance, adaptability, and
+cost. This paper addresses these needs by proposing a shared backbone model
+architecture enhanced with lightweight LoRA adapters for task-specific
+fine-tuning, targeting the automated scoring of student responses across 27
+mutually exclusive tasks. By achieving competitive performance (average QWK of
+0.848 compared to 0.888 for fully fine-tuned models) while reducing GPU memory
+consumption by 60% and inference latency by 40%, the framework demonstrates
+significant efficiency gains. This approach aligns with the workshops' focus on
+improving language models for educational tasks, creating responsible
+innovations for cost-sensitive deployment, and supporting educators by
+streamlining assessment workflows. The findings underscore the potential of
+scalable AI to enhance learning outcomes while maintaining fairness and
+transparency in automated scoring systems.
+
+
+
+ comment: Accepted by AAAI-iRAISE Workshop
+
+
+
+ + ☆ TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow + Matching and Clap-Ranked Preference Optimization +
+
+
+
+
+
+
+
+ Chia-Yu Hung, Navonil Majumder, Zhifeng Kong, Ambuj Mehrish, Rafael Valle, Bryan Catanzaro, Soujanya Poria
+
+
+ We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model
+with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio
+in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models
+lies in the difficulty of creating preference pairs, as TTA lacks structured
+mechanisms like verifiable rewards or gold-standard answers available for Large
+Language Models (LLMs). To address this, we propose CLAP-Ranked Preference
+Optimization (CRPO), a novel framework that iteratively generates and optimizes
+preference data to enhance TTA alignment. We demonstrate that the audio
+preference dataset generated using CRPO outperforms existing alternatives. With
+this framework, TangoFlux achieves state-of-the-art performance across both
+objective and subjective benchmarks. We open source all code and models to
+support further research in TTA generation.
+
+
+
+ comment: https://tangoflux.github.io/
+
+
+
+ + ☆ GePBench: Evaluating Fundamental Geometric Perception for Multimodal + Large Language Models +
+
+
+
+
+
+
+
+ Shangyu Xing, Changhao Xiang, Yuteng Han, Yifan Yue, Zhen Wu, Xinyu Liu, Zhangtai Wu, Fei Zhao, Xinyu Dai
+
+
+ Multimodal large language models (MLLMs) have achieved significant
+advancements in integrating visual and linguistic understanding. While existing
+benchmarks evaluate these models in context-rich, real-life scenarios, they
+often overlook fundamental perceptual skills essential for environments
+deviating from everyday realism. In particular, geometric perception, the
+ability to interpret spatial relationships and abstract visual patterns,
+remains underexplored. To address this limitation, we introduce GePBench, a
+novel benchmark designed to assess the geometric perception capabilities of
+MLLMs. Results from extensive evaluations reveal that current state-of-the-art
+MLLMs exhibit significant deficiencies in such tasks. Additionally, we
+demonstrate that models trained with data sourced from GePBench show notable
+improvements on a wide range of downstream tasks, underscoring the importance
+of geometric perception as a foundation for advanced multimodal applications.
+Our code and datasets will be publicly available.
+
+
+
+
+ + ☆ Plancraft: an evaluation dataset for planning with LLM agents +
+ +
+ We present Plancraft, a multi-modal evaluation dataset for LLM agents.
+Plancraft has both a text-only and multi-modal interface, based on the
+Minecraft crafting GUI. We include the Minecraft Wiki to evaluate tool use and
+Retrieval Augmented Generation (RAG), as well as an oracle planner and oracle
+RAG information extractor, to ablate the different components of a modern agent
+architecture. To evaluate decision-making, Plancraft also includes a subset of
+examples that are intentionally unsolvable, providing a realistic challenge
+that requires the agent not only to complete tasks but also to decide whether
+they are solvable at all. We benchmark both open-source and closed-source LLMs
+and strategies on our task and compare their performance to a handcrafted
+planner. We find that LLMs and VLMs struggle with the planning problems that
+Plancraft introduces, and we offer suggestions on how to improve their
+capabilities.
+
+
+
+
+ + ☆ MapQaTor: A System for Efficient Annotation of Map Query Datasets +
+ +
+ Mapping and navigation services like Google Maps, Apple Maps, Openstreet
+Maps, are essential for accessing various location-based data, yet they often
+struggle to handle natural language geospatial queries. Recent advancements in
+Large Language Models (LLMs) show promise in question answering (QA), but
+creating reliable geospatial QA datasets from map services remains challenging.
+We introduce MapQaTor, a web application that streamlines the creation of
+reproducible, traceable map-based QA datasets. With its plug-and-play
+architecture, MapQaTor enables seamless integration with any maps API, allowing
+users to gather and visualize data from diverse sources with minimal setup. By
+caching API responses, the platform ensures consistent ground truth, enhancing
+the reliability of the data even as real-world information evolves. MapQaTor
+centralizes data retrieval, annotation, and visualization within a single
+platform, offering a unique opportunity to evaluate the current state of
+LLM-based geospatial reasoning while advancing their capabilities for improved
+geospatial understanding. Evaluation metrics show that, MapQaTor speeds up the
+annotation process by at least 30 times compared to manual methods,
+underscoring its potential for developing geospatial resources, such as complex
+map reasoning datasets. The website is live at: https://mapqator.github.io/ and
+a demo video is available at: https://youtu.be/7_aV9Wmhs6Q.
+
+
+
+ comment: 13 pages, 35 figures
+
+
+
+ + ☆ Verbosity-Aware Rationale Reduction: Effective Reduction of Redundant + Rationale via Principled Criteria +
+ +
+ Large Language Models (LLMs) rely on generating extensive intermediate
+reasoning units (e.g., tokens, sentences) to enhance final answer quality
+across a wide range of complex tasks. While generating multiple reasoning paths
+or iteratively refining rationales proves effective for improving performance,
+these approaches inevitably result in significantly higher inference costs. In
+this work, we propose a novel sentence-level rationale reduction training
+framework that leverages likelihood-based criteria, verbosity, to identify and
+remove redundant reasoning sentences. Unlike previous approaches that utilize
+token-level reduction, our sentence-level reduction framework maintains model
+performance while reducing generation length. This preserves the original
+reasoning abilities of LLMs and achieves an average 17.15% reduction in
+generation costs across various models and tasks.
+
+
+
+
+ + ☆ Plug-and-Play Training Framework for Preference Optimization +
+ +
+ Recently, preference optimization methods such as DPO have significantly
+enhanced large language models (LLMs) in wide tasks including dialogue and
+question-answering. However, current methods fail to account for the varying
+difficulty levels of training samples during preference optimization, leading
+to mediocre performance in tasks with high accuracy requirements, particularly
+in mathematical reasoning. To address this limitation, we propose a novel
+training framework, which employs multiple sampling to analyze output
+distributions, assign different weights to samples, and incorporate these
+weights into the preference optimization process. This plug-and-play approach
+enables LLMs to prioritize challenging examples during training, improving
+learning efficiency. Experimental results demonstrate that our framework
+integrates seamlessly with various preference optimization methods and achieves
+consistent improvements in mathematical reasoning tasks.
+
+
+
+ comment: 12 pages, 9 figures
+
+
+
+ + ☆ KARPA: A Training-free Method of Adapting Knowledge Graph as References + for Large Language Model's Reasoning Path Aggregation +
+
+
+
+
+
+
+
+ Siyuan Fang, Kaijing Ma, Tianyu Zheng, Xinrun Du, Ningxuan Lu, Ge Zhang, Qingkun Tang
+
+
+ Large language models (LLMs) demonstrate exceptional performance across a
+variety of tasks, yet they are often affected by hallucinations and the
+timeliness of knowledge. Leveraging knowledge graphs (KGs) as external
+knowledge sources has emerged as a viable solution, but existing methods for
+LLM-based knowledge graph question answering (KGQA) are often limited by
+step-by-step decision-making on KGs, restricting the global planning and
+reasoning capabilities of LLMs, or they require fine-tuning or pre-training on
+specific KGs. To address these challenges, we propose Knowledge graph Assisted
+Reasoning Path Aggregation (KARPA), a novel framework that harnesses the global
+planning abilities of LLMs for efficient and accurate KG reasoning. KARPA
+operates in three steps: pre-planning relation paths using the LLM's global
+planning capabilities, matching semantically relevant paths via an embedding
+model, and reasoning over these paths to generate answers. Unlike existing KGQA
+methods, KARPA avoids stepwise traversal, requires no additional training, and
+is adaptable to various LLM architectures. Extensive experimental results show
+that KARPA achieves state-of-the-art performance in KGQA tasks, delivering both
+high efficiency and accuracy. Our code will be available on Github.
+
+
+
+ comment: 23 pages, 6 figures
+
+
+
+ + ☆ Efficiently Serving LLM Reasoning Programs with Certaindex +
+
+
+
+
+
+
+
+ Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu, Zhongdongming Dai, Aurick Qiao, Hao Zhang
+
+
+ The rapid evolution of large language models (LLMs) has unlocked their
+capabilities in advanced reasoning tasks like mathematical problem-solving,
+code generation, and legal analysis. Central to this progress are
+inference-time reasoning algorithms, which refine outputs by exploring multiple
+solution paths, at the cost of increasing compute demands and response
+latencies. Existing serving systems fail to adapt to the scaling behaviors of
+these algorithms or the varying difficulty of queries, leading to inefficient
+resource use and unmet latency targets.
+ We present Dynasor, a system that optimizes inference-time compute for LLM
+reasoning queries. Unlike traditional engines, Dynasor tracks and schedules
+requests within reasoning queries and uses Certaindex, a proxy that measures
+statistical reasoning progress based on model certainty, to guide compute
+allocation dynamically. Dynasor co-adapts scheduling with reasoning progress:
+it allocates more compute to hard queries, reduces compute for simpler ones,
+and terminates unpromising queries early, balancing accuracy, latency, and
+cost. On diverse datasets and algorithms, Dynasor reduces compute by up to 50%
+in batch processing and sustaining 3.3x higher query rates or 4.7x tighter
+latency SLOs in online serving.
+
+
+
+
+ + ☆ DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models +
+ +
+ Low-rank adaptation (LoRA) reduces the computational and memory demands of
+fine-tuning large language models (LLMs) by approximating updates with low-rank
+matrices. However, low-rank approximation in two-dimensional space fails to
+capture high-dimensional structures within the target matrix. Recently, tensor
+decomposition methods have been explored for fine-tuning LLMs, leveraging their
+ability to extract structured information. Yet, these approaches primarily rely
+on random initialization, and the impact of initialization on tensor adaptation
+remains underexplored. In this paper, we reveal that random initialization
+significantly diverges from the validation loss achieved by full fine-tuning.
+To address this, we propose Weight-Decomposed Tensor Adaptation (DoTA), which
+leverages the Matrix Product Operator (MPO) decomposition of pre-trained
+weights for effective initialization in fine-tuning LLMs. Additionally, we
+introduce QDoTA, a quantized version of DoTA designed for 4-bit quantization.
+Experiments on commonsense and arithmetic reasoning tasks show that DoTA
+outperforms random initialization methods with fewer parameters. QDoTA further
+reduces memory consumption and achieves comparable performance to DoTA on
+commonsense reasoning tasks. We will release our code to support future
+research.
+
+
+
+ comment: 12 pages, 6 figures
+
+
+
+ + ☆ Enhancing Annotated Bibliography Generation with LLM Ensembles +
+ +
+ This work proposes a novel approach to enhancing annotated bibliography
+generation through Large Language Model (LLM) ensembles. In particular,
+multiple LLMs in different roles -- controllable text generation, evaluation,
+and summarization -- are introduced and validated using a systematic
+methodology to enhance model performance in scholarly tasks. Output diversity
+among the ensemble that generates text is obtained using different LLM
+parameters, followed by an LLM acting as a judge to assess relevance, accuracy,
+and coherence. Responses selected by several combining strategies are then
+merged and refined through summarization and redundancy removal techniques. The
+preliminary experimental validation demonstrates that the combined outputs from
+the LLM ensemble improve coherence and relevance compared to individual
+responses, leading to a 38% improvement in annotation quality and a 51%
+reduction in content redundancy, thus highlighting the potential for automating
+complex scholarly tasks while maintaining high-quality standards.
+
+
+
+
+ + ☆ Are LLMs Really Not Knowledgable? Mining the Submerged Knowledge in + LLMs' Memory +
+ +
+ Large language models (LLMs) have shown promise as potential knowledge bases,
+yet they often struggle with question-answering tasks and are prone to
+hallucinations. While previous research attributes these issues to knowledge
+gaps in the model's parameters, our investigation reveals a different
+phenomenon: LLMs often retain correct knowledge even when generating incorrect
+answers. Through analysis of model's internal representations, we find that
+correct answers frequently appear among high-probability tokens despite not
+being selected as final outputs. Based on this observation, we introduce
+Hits@k, a new metric to assess knowledge retention independent of expression
+accuracy. Our extensive experiments demonstrate that LLMs store significantly
+more knowledge than their QA performance suggests. Building on these findings,
+we develop SkipUnsure, a method to improve answer accuracy by leveraging
+detected but unexpressed knowledge. Experiments on both open-domain and
+specific-domain datasets show consistent improvements, with accuracy gains of
+up to 11.8% on DBPedia and 6.3% on IMDB, without requiring model retraining.
+
+
+
+
+ + ☆ Disentangling Preference Representation and Text Generation for + Efficient Individual Preference Alignment +
+ +
+ Aligning Large Language Models (LLMs) with general human preferences has been
+proved crucial in improving the interaction quality between LLMs and human.
+However, human values are inherently diverse among different individuals,
+making it insufficient to align LLMs solely with general preferences. To
+address this, personalizing LLMs according to individual feedback emerges as a
+promising solution. Nonetheless, this approach presents challenges in terms of
+the efficiency of alignment algorithms. In this work, we introduce a flexible
+paradigm for individual preference alignment. Our method fundamentally improves
+efficiency by disentangling preference representation from text generation in
+LLMs. We validate our approach across multiple text generation tasks and
+demonstrate that it can produce aligned quality as well as or better than
+PEFT-based methods, while reducing additional training time for each new
+individual preference by $80\%$ to $90\%$ in comparison with them.
+
+
+
+ comment: Coling 2025
+
+
+
+ + ☆ Enhancing Multimodal Emotion Recognition through Multi-Granularity + Cross-Modal Alignment ICASSP 2025 +
+ +
+ Multimodal emotion recognition (MER), leveraging speech and text, has emerged
+as a pivotal domain within human-computer interaction, demanding sophisticated
+methods for effective multimodal integration. The challenge of aligning
+features across these modalities is significant, with most existing approaches
+adopting a singular alignment strategy. Such a narrow focus not only limits
+model performance but also fails to address the complexity and ambiguity
+inherent in emotional expressions. In response, this paper introduces a
+Multi-Granularity Cross-Modal Alignment (MGCMA) framework, distinguished by its
+comprehensive approach encompassing distribution-based, instance-based, and
+token-based alignment modules. This framework enables a multi-level perception
+of emotional information across modalities. Our experiments on IEMOCAP
+demonstrate that our proposed method outperforms current state-of-the-art
+techniques.
+
+
+
+ comment: ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech
+ and Signal Processing (ICASSP)
+
+
+
+ + ☆ Attributing Culture-Conditioned Generations to Pretraining Corpora +
+ +
+ In open-ended generative tasks like narrative writing or dialogue, large
+language models often exhibit cultural biases, showing limited knowledge and
+generating templated outputs for less prevalent cultures. Recent works show
+that these biases may stem from uneven cultural representation in pretraining
+corpora. This work investigates how pretraining leads to biased
+culture-conditioned generations by analyzing how models associate entities with
+cultures based on pretraining data patterns. We propose the MEMOed framework
+(MEMOrization from pretraining document) to determine whether a generation for
+a culture arises from memorization. Using MEMOed on culture-conditioned
+generations about food and clothing for 110 cultures, we find that
+high-frequency cultures in pretraining data yield more generations with
+memorized symbols, while some low-frequency cultures produce none.
+Additionally, the model favors generating entities with extraordinarily high
+frequency regardless of the conditioned culture, reflecting biases toward
+frequent pretraining terms irrespective of relevance. We hope that the MEMOed
+framework and our insights will inspire more works on attributing model
+performance on pretraining data.
+
+
+
+
+ + ☆ Depression and Anxiety Prediction Using Deep Language Models and + Transfer Learning +
+
+
+
+
+
+
+
+ Tomasz Rutowski, Elizabeth Shriberg, Amir Harati, Yang Lu, Piotr Chlebek, Ricardo Oliveira
+
+
+ Digital screening and monitoring applications can aid providers in the
+management of behavioral health conditions. We explore deep language models for
+detecting depression, anxiety, and their co-occurrence from conversational
+speech collected during 16k user interactions with an application. Labels come
+from PHQ-8 and GAD-7 results also collected by the application. We find that
+results for binary classification range from 0.86 to 0.79 AUC, depending on
+condition and co-occurrence. Best performance is achieved when a user has
+either both or neither condition, and we show that this result is not
+attributable to data skew. Finally, we find evidence suggesting that underlying
+word sequence cues may be more salient for depression than for anxiety.
+
+
+
+
+ + ☆ HUNYUANPROVER: A Scalable Data Synthesis Framework and Guided Tree + Search for Automated Theorem Proving +
+ +
+ We introduce HunyuanProver, an language model finetuned from the Hunyuan 7B
+for interactive automatic theorem proving with LEAN4. To alleviate the data
+sparsity issue, we design a scalable framework to iterative synthesize data
+with low cost. Besides, guided tree search algorithms are designed to enable
+effective ``system 2 thinking`` of the prover. HunyuanProver achieves
+state-of-the-art (SOTA) performances on major benchmarks. Specifically, it
+achieves a pass of 68.4% on the miniF2F-test compared to 65.9%, the current
+SOTA results. It proves 4 IMO statements (imo_1960_p2, imo_1962_p2},
+imo_1964_p2 and imo_1983_p6) in miniF2F-test. To benefit the community, we will
+open-source a dataset of 30k synthesized instances, where each instance
+contains the original question in natural language, the converted statement by
+autoformalization, and the proof by HunyuanProver.
+
+
+
+
+ + ☆ ChartAdapter: Large Vision-Language Model for Chart Summarization +
+ +
+ Chart summarization, which focuses on extracting key information from charts
+and interpreting it in natural language, is crucial for generating and
+delivering insights through effective and accessible data analysis. Traditional
+methods for chart understanding and summarization often rely on multi-stage
+pipelines, which may produce suboptimal semantic alignment between visual and
+textual information. In comparison, recently developed LLM-based methods are
+more dependent on the capability of foundation images or languages, while
+ignoring the characteristics of chart data and its relevant challenges. To
+address these limitations, we propose ChartAdapter, a novel lightweight
+transformer module designed to bridge the gap between charts and textual
+summaries. ChartAdapter employs learnable query vectors to extract implicit
+semantics from chart data and incorporates a cross-modal alignment projector to
+enhance vision-to-language generative learning. By integrating ChartAdapter
+with an LLM, we enable end-to-end training and efficient chart summarization.
+To further enhance the training, we introduce a three-stage hierarchical
+training procedure and develop a large-scale dataset specifically curated for
+chart summarization, comprising 190,618 samples. Experimental results on the
+standard Chart-to-Text testing set demonstrate that our approach significantly
+outperforms existing methods, including state-of-the-art models, in generating
+high-quality chart summaries. Ablation studies further validate the
+effectiveness of key components in ChartAdapter. This work highlights the
+potential of tailored LLM-based approaches to advance chart understanding and
+sets a strong foundation for future research in this area.
+
+
+
+
+ + ☆ UBER: Uncertainty-Based Evolution with Large Language Models for + Automatic Heuristic Design +
+ +
+ NP-hard problem-solving traditionally relies on heuristics, but manually
+crafting effective heuristics for complex problems remains challenging. While
+recent work like FunSearch has demonstrated that large language models (LLMs)
+can be leveraged for heuristic design in evolutionary algorithm (EA)
+frameworks, their potential is not fully realized due to its deficiency in
+exploitation and exploration. We present UBER (Uncertainty-Based Evolution for
+Refinement), a method that enhances LLM+EA methods for automatic heuristic
+design by integrating uncertainty on top of the FunSearch framework. UBER
+introduces two key innovations: an Uncertainty-Inclusive Evolution Process
+(UIEP) for adaptive exploration-exploitation balance, and a principled
+Uncertainty-Inclusive Island Reset (UIIS) strategy for maintaining population
+diversity. Through extensive experiments on challenging NP-complete problems,
+UBER demonstrates significant improvements over FunSearch. Our work provides a
+new direction for the synergy of LLMs and EA, advancing the field of automatic
+heuristic design.
+
+
+
+
+ + ☆ Align Attention Heads Before Merging Them: An Effective Way for + Converting MHA to GQA +
+ +
+ Large language models have been shown to perform well on a variety of natural
+language processing problems. However, as the model size and the input
+sequence's length increase, the rapid increase of KV Cache significantly slows
+down inference speed. Therefore GQA model, as an alternative to MHA model, has
+been widely introduced into LLMs. In this work, we propose a low-cost method
+for pruning MHA models into GQA models with any compression ratio of key-value
+heads. Our method is based on $\mathit{L_0}$ masks to gradually remove
+redundant parameters. In addition, we apply orthogonal transformations to
+attention heads without changing the model to increase similarity between
+attention heads before pruning training, in order to further improve
+performance of the model. Our method can be compatible with rotary position
+embedding (RoPE), which means the model after training can be fully adapted to
+the mainstream standard GQA framework. Experiments demonstrate that our
+strategy can compress up to 87.5% of key-value heads of the LLaMA2-7B model
+without too much performance degradation, just achieved through supervised
+fine-tuning.
+
+
+
+ comment: 12 pages, 4 figures
+
+
+
+ + ☆ Knowledge Editing for Large Language Model with Knowledge Neuronal + Ensemble +
+ +
+ As real-world knowledge is constantly evolving, ensuring the timeliness and
+accuracy of a model's knowledge is crucial. This has made knowledge editing in
+large language models increasingly important. However, existing knowledge
+editing methods face several challenges, including parameter localization
+coupling, imprecise localization, and a lack of dynamic interaction across
+layers. In this paper, we propose a novel knowledge editing method called
+Knowledge Neuronal Ensemble (KNE). A knowledge neuronal ensemble represents a
+group of neurons encoding specific knowledge, thus mitigating the issue of
+frequent parameter modification caused by coupling in parameter localization.
+The KNE method enhances the precision and accuracy of parameter localization by
+computing gradient attribution scores for each parameter at each layer. During
+the editing process, only the gradients and losses associated with the
+knowledge neuronal ensemble are computed, with error backpropagation performed
+accordingly, ensuring dynamic interaction and collaborative updates among
+parameters. Experimental results on three widely used knowledge editing
+datasets show that the KNE method significantly improves the accuracy of
+knowledge editing and achieves, or even exceeds, the performance of the best
+baseline methods in portability and locality metrics.
+
+
+
+ comment: 26 pages, 5 figures, 2 tables
+
+
+
+ + ☆ GASLITEing the Retrieval: Exploring Vulnerabilities in Dense + Embedding-based Search +
+ +
+ Dense embedding-based text retrieval$\unicode{x2013}$retrieval of relevant
+passages from corpora via deep learning encodings$\unicode{x2013}$has emerged
+as a powerful method attaining state-of-the-art search results and popularizing
+the use of Retrieval Augmented Generation (RAG). Still, like other search
+methods, embedding-based retrieval may be susceptible to search-engine
+optimization (SEO) attacks, where adversaries promote malicious content by
+introducing adversarial passages to corpora. To faithfully assess and gain
+insights into the susceptibility of such systems to SEO, this work proposes the
+GASLITE attack, a mathematically principled gradient-based search method for
+generating adversarial passages without relying on the corpus content or
+modifying the model. Notably, GASLITE's passages (1) carry adversary-chosen
+information while (2) achieving high retrieval ranking for a selected query
+distribution when inserted to corpora. We use GASLITE to extensively evaluate
+retrievers' robustness, testing nine advanced models under varied threat
+models, while focusing on realistic adversaries targeting queries on a specific
+concept (e.g., a public figure). We found GASLITE consistently outperformed
+baselines by $\geq$140% success rate, in all settings. Particularly,
+adversaries using GASLITE require minimal effort to manipulate search
+results$\unicode{x2013}$by injecting a negligible amount of adversarial
+passages ($\leq$0.0001% of the corpus), they could make them visible in the
+top-10 results for 61-100% of unseen concept-specific queries against most
+evaluated models. Inspecting variance in retrievers' robustness, we identify
+key factors that may contribute to models' susceptibility to SEO, including
+specific properties in the embedding space's geometry.
+
+
+
+
+ + ♻ ☆ Order Matters in Hallucination: Reasoning Order as Benchmark and + Reflexive Prompting for Large-Language-Models ACL22025 +
+ +
+ Large language models (LLMs) have generated significant attention since their
+inception, finding applications across various academic and industrial domains.
+However, these models often suffer from the "hallucination problem", where
+outputs, though grammatically and logically coherent, lack factual accuracy or
+are entirely fabricated. A particularly troubling issue discovered and widely
+discussed recently is the numerical comparison error where multiple LLMs
+incorrectly infer that "9.11$>$9.9". We discovered that the order in which LLMs
+generate answers and reasoning impacts their consistency. Specifically, results
+vary significantly when an LLM generates an answer first and then provides the
+reasoning versus generating the reasoning process first and then the
+conclusion. Inspired by this, we propose a new benchmark method for assessing
+LLM consistency: comparing responses generated through these two different
+approaches. This benchmark effectively identifies instances where LLMs
+fabricate answers and subsequently generate justifications. Furthermore, we
+introduce a novel and straightforward prompt strategy designed to mitigate this
+issue. Experimental results demonstrate that this strategy improves performance
+across various LLMs compared to direct questioning. This work not only sheds
+light on a critical flaw in LLMs but also offers a practical solution to
+enhance their reliability.
+
+
+
+ comment: 8 pages, submitted to ACL22025
+
+
+
+ + ♻ ☆ ReXTrust: A Model for Fine-Grained Hallucination Detection in + AI-Generated Radiology Reports +
+ +
+ The increasing adoption of AI-generated radiology reports necessitates robust
+methods for detecting hallucinations--false or unfounded statements that could
+impact patient care. We present ReXTrust, a novel framework for fine-grained
+hallucination detection in AI-generated radiology reports. Our approach
+leverages sequences of hidden states from large vision-language models to
+produce finding-level hallucination risk scores. We evaluate ReXTrust on a
+subset of the MIMIC-CXR dataset and demonstrate superior performance compared
+to existing approaches, achieving an AUROC of 0.8751 across all findings and
+0.8963 on clinically significant findings. Our results show that white-box
+approaches leveraging model hidden states can provide reliable hallucination
+detection for medical AI systems, potentially improving the safety and
+reliability of automated radiology reporting.
+
+
+
+ comment: Accepted to AIMedHealth 10 pages, 5 figures
+
+
+
+ + ♻ ☆ LLM Distillation for Efficient Few-Shot Multiple Choice Question + Answering +
+ +
+ Multiple Choice Question Answering (MCQA) is an important problem with
+numerous real-world applications, such as medicine, law, and education. The
+high cost of building MCQA datasets makes few-shot learning pivotal in this
+domain. While Large Language Models (LLMs) can enable few-shot learning, their
+direct application in real-world scenarios is often hindered by their high
+computational cost. To address this challenge, we propose a simple yet
+effective approach that uses LLMs for data generation and scoring. Our approach
+utilizes LLMs to create MCQA data which contains questions and choices, and to
+assign probability scores to the generated choices. We then use the generated
+data and LLM-assigned scores to finetune a smaller and more efficient
+encoder-only model, DeBERTa-v3-base by leveraging distillation loss. Extensive
+experiments on the Massive Multitask Language Understanding (MMLU) benchmark
+demonstrate that our method improves accuracy from 28.9% to 39.3%, representing
+a gain of over 10% compared to a baseline finetuned directly on 5-shot
+examples. This shows the effectiveness of LLM-driven data generation and
+knowledge distillation for few-shot MCQA.
+
+
+
+
+ + ♻ ☆ DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought +
+ +
+ Recently, O1-like models have emerged as representative examples,
+illustrating the effectiveness of long chain-of-thought (CoT) in reasoning
+tasks such as math and coding tasks. In this paper, we introduce DRT-o1, an
+attempt to bring the success of long CoT to neural machine translation (MT).
+Specifically, in view of the literature books that might involve similes and
+metaphors, translating these texts to a target language is very difficult in
+practice due to cultural differences. In such cases, literal translation often
+fails to convey the intended meaning effectively. Even for professional human
+translators, considerable thought must be given to preserving semantics
+throughout the translation process. To simulate LLMs' long thought ability in
+MT, we first mine sentences containing similes or metaphors from existing
+literature books, and then develop a multi-agent framework to translate these
+sentences via long thought. In the multi-agent framework, a translator is used
+to iteratively translate the source sentence under the suggestions provided by
+an advisor. To ensure the effectiveness of the long thoughts, an evaluator is
+also employed to quantify the translation in each round. In this way, we
+collect tens of thousands of long-thought MT data, which is used to train our
+DRT-o1. Using Qwen2.5 and LLama-3.1 as the backbones, DRT-o1 models can learn
+the thought process during machine translation, and outperform vanilla LLMs as
+well as existing O1-like LLMs, showing their effectiveness The project is
+available at https://github.com/krystalan/DRT-o1
+
+
+
+
+ + ♻ ☆ CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models + of Code +
+ +
+ Large Language Models (LLMs) have achieved remarkable progress in code
+generation. It now becomes crucial to identify whether the code is AI-generated
+and to determine the specific model used, particularly for purposes such as
+protecting Intellectual Property (IP) in industry and preventing cheating in
+programming exercises. To this end, several attempts have been made to insert
+watermarks into machine-generated code. However, existing approaches are
+limited to inserting only a single bit of information. In this paper, we
+introduce CodeIP, a novel multi-bit watermarking technique that inserts
+additional information to preserve crucial provenance details, such as the
+vendor ID of an LLM, thereby safeguarding the IPs of LLMs in code generation.
+Furthermore, to ensure the syntactical correctness of the generated code, we
+propose constraining the sampling process for predicting the next token by
+training a type predictor. Experiments conducted on a real-world dataset across
+five programming languages demonstrate the effectiveness of CodeIP in
+watermarking LLMs for code generation while maintaining the syntactical
+correctness of code.
+
+
+
+ comment: 16 pages, 13 figures
+
+
+
+ + ♻ ☆ A Comprehensive Survey of Large Language Models and Multimodal Large + Language Models in Medicine +
+
+
+
+
+
+
+
+ Hanguang Xiao, Feizhong Zhou, Xingyue Liu, Tianqi Liu, Zhipeng Li, Xin Liu, Xiaoxuan Huang
+
+
+ Since the release of ChatGPT and GPT-4, large language models (LLMs) and
+multimodal large language models (MLLMs) have attracted widespread attention
+for their exceptional capabilities in understanding, reasoning, and generation,
+introducing transformative paradigms for integrating artificial intelligence
+into medicine. This survey provides a comprehensive overview of the
+development, principles, application scenarios, challenges, and future
+directions of LLMs and MLLMs in medicine. Specifically, it begins by examining
+the paradigm shift, tracing the transition from traditional models to LLMs and
+MLLMs, and highlighting the unique advantages of these LLMs and MLLMs in
+medical applications. Next, the survey reviews existing medical LLMs and MLLMs,
+providing detailed guidance on their construction and evaluation in a clear and
+systematic manner. Subsequently, to underscore the substantial value of LLMs
+and MLLMs in healthcare, the survey explores five promising applications in the
+field. Finally, the survey addresses the challenges confronting medical LLMs
+and MLLMs and proposes practical strategies and future directions for their
+integration into medicine. In summary, this survey offers a comprehensive
+analysis of the technical methodologies and practical clinical applications of
+medical LLMs and MLLMs, with the goal of bridging the gap between these
+advanced technologies and clinical practice, thereby fostering the evolution of
+the next generation of intelligent healthcare systems.
+
+
+
+
+ + ♻ ☆ SepLLM: Accelerate Large Language Models by Compressing One Segment into + One Separator +
+
+
+
+
+
+
+
+ Guoxuan Chen, Han Shi, Jiawei Li, Yihang Gao, Xiaozhe Ren, Yimeng Chen, Xin Jiang, Zhenguo Li, Weiyang Liu, Chao Huang
+
+
+ Large Language Models (LLMs) have exhibited exceptional performance across a
+spectrum of natural language processing tasks. However, their substantial sizes
+pose considerable challenges, particularly in computational demands and
+inference speed, due to their quadratic complexity. In this work, we have
+identified a key pattern: certain seemingly meaningless special tokens (i.e.,
+separators) contribute disproportionately to attention scores compared to
+semantically meaningful tokens. This observation suggests that information of
+the segments between these separator tokens can be effectively condensed into
+the separator tokens themselves without significant information loss. Guided by
+this insight, we introduce SepLLM, a plug-and-play framework that accelerates
+inference by compressing these segments and eliminating redundant tokens.
+Additionally, we implement efficient kernels for training acceleration.
+Experimental results across training-free, training-from-scratch, and
+post-training settings demonstrate SepLLM's effectiveness. Notably, using the
+Llama-3-8B backbone, SepLLM achieves over 50% reduction in KV cache on the
+GSM8K-CoT benchmark while maintaining comparable performance. Furthermore, in
+streaming settings, SepLLM effectively processes sequences of up to 4 million
+tokens or more while maintaining consistent language modeling capabilities.
+
+
+
+ comment: We have made our code publicly available at sepllm.github.io. Our
+ codebase supports efficient multi-node distributed training with accelerated
+ attention module Sep-Attention and also supports numerous existing Fusion
+ Operators to accelerate the training process, such as fused rope, etc. If you
+ find our code helpful, please kindly consider giving us a **star** on
+ GitHub^_^. Thank you very much!
+
+
+
+ + ♻ ☆ MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct +
+
+
+
+
+
+
+
+ Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Xiaobo Xia, Fei Huang, Jingkuan Song, Yongbin Li
+
+
+ The development of Multimodal Large Language Models (MLLMs) has seen
+significant advancements with increasing demands in various fields (e.g.,
+multimodal agents, embodied intelligence). While model-driven approaches
+attempt to enhance MLLMs capabilities through diverse architectures, the gains
+have become increasingly marginal. Conversely, data-driven methods, which scale
+up image-text instruction data, are more effective but face limited data
+diversity and complexity challenges. The absence of high-quality data
+constitutes a significant development barrier for MLLMs. To address the data
+quality bottleneck, we propose MMEvol, a novel multimodal instruction data
+evolution framework. This framework iteratively improve data quality through a
+refined combination of fine-grained perception, cognitive reasoning, and
+interaction evolution, generating a more complex and diverse image-text
+instruction dataset that empowers MLLMs with enhanced capabilities. Beginning
+with an initial set of instructions, SEED-163K, we utilize MMEvol to
+systematically broaden the diversity of instruction types, extend visual
+reasoning steps to improve cognitive reasoning abilities, and thoroughly
+explore fine-grained information within images to enhance visual understanding
+and robustness. To comprehensively evaluate the effectiveness of our approach,
+we conduct extensive qualitative analysis and quantitative experiments across
+13 vision-language tasks. Compared to baseline models trained with the initial
+seed data, the results demonstrate that our method achieves an average accuracy
+improvement of 3.1 percentage points. Furthermore, our approach reaches
+state-of-the-art (SOTA) performance in nine tasks using significantly less data
+compared to state-of-the-art models.
+
+
+
+
+ + ♻ ☆ Nash CoT: Multi-Path Inference with Preference Equilibrium +
+ +
+ Chain of thought (CoT) is a reasoning framework that can enhance the
+performance of Large Language Models (LLMs) on complex inference tasks. In
+particular, among various studies related to CoT, multi-path inference stands
+out as a simple yet effective improvement. However, there is no optimal setting
+for the number of inference paths. Therefore, we have to increase the number of
+inference paths to obtain better results, which in turn increases the inference
+cost. To address this limitation, we can utilize question-related role
+templates to guide LLMs into relevant roles, thereby increasing the possibility
+of correct inferences for each path and further reducing dependence on the
+number of inference paths while improving reasoning accuracy. However, placing
+LLMs into specific roles may reduce their reasoning diversity and performance
+on a few tasks where role dependence is low. To alleviate the excessive
+immersion of the LLM into a specific role, we propose Nash CoT by constructing
+a game system on each path that balances the generation from role-specific
+LLMs' and the general LLMs' generation, thereby ensuring both effective role
+adoption and diversity in LLM generation further maintaining the performance of
+multi-path inference while reducing the requirement of the number of inference
+paths. We evaluate Nash CoT across various inference tasks, including Arabic
+Reasoning, Commonsense Question Answering, and Symbolic Inference, achieving
+results that are comparable to or better than those of multi-path CoT with the
+equal number of inference paths.
+
+
+
+
+ + ♻ ☆ JuniperLiu at CoMeDi Shared Task: Models as Annotators in Lexical + Semantics Disagreements +
+ +
+ We present the results of our system for the CoMeDi Shared Task, which
+predicts majority votes (Subtask 1) and annotator disagreements (Subtask 2).
+Our approach combines model ensemble strategies with MLP-based and
+threshold-based methods trained on pretrained language models. Treating
+individual models as virtual annotators, we simulate the annotation process by
+designing aggregation measures that incorporate continuous relatedness scores
+and discrete classification labels to capture both majority and disagreement.
+Additionally, we employ anisotropy removal techniques to enhance performance.
+Experimental results demonstrate the effectiveness of our methods, particularly
+for Subtask 2. Notably, we find that standard deviation on continuous
+relatedness scores among different model manipulations correlates with human
+disagreement annotations compared to metrics on aggregated discrete labels. The
+code will be published at https://github.com/RyanLiut/CoMeDi_Solution.
+
+
+
+ comment: accepted by CoMeDi workshop in Coling 2025
+
+
+
+ + ♻ ☆ An Empirical Study of Catastrophic Forgetting in Large Language Models + During Continual Fine-tuning +
+ +
+ Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning
+when a model forgets previously learned information while acquiring new
+knowledge for achieving a satisfactory performance in downstream tasks. As
+large language models (LLMs) have demonstrated remarkable performance, it is
+intriguing to investigate whether CF exists during the continual instruction
+tuning of LLMs. This study empirically evaluates the forgetting phenomenon in
+LLMs' knowledge during continual instruction tuning from the perspectives of
+domain knowledge, reasoning, and reading comprehension. The experiments reveal
+that catastrophic forgetting is generally observed in LLMs ranging from 1b to
+7b parameters. Surprisingly, as the model scale increases, the severity of
+forgetting intensifies in such a model sale range which may result from the
+much significant initial performance in the larger LLM. Comparing the
+decoder-only model BLOOMZ with the encoder-decoder model mT0, BLOOMZ exhibits
+less forgetting and retains more knowledge. Interestingly, we also observe that
+LLMs can mitigate language biases, such as gender bias, during continual
+fine-tuning. Furthermore, our findings indicate that general instruction tuning
+can help alleviate the forgetting phenomenon in LLMs during subsequent
+fine-tuning.
+
+
+
+
+ + ♻ ☆ Distilling Fine-grained Sentiment Understanding from Large Language + Models +
+
+
+
+
+
+
+
+ Yice Zhang, Guangyu Xie, Hongling Xu, Kaiheng Hou, Jianzhu Bao, Qianlong Wang, Shiwei Chen, Ruifeng Xu
+
+
+ Fine-grained sentiment analysis (FSA) aims to extract and summarize user
+opinions from vast opinionated text. Recent studies demonstrate that large
+language models (LLMs) possess exceptional sentiment understanding
+capabilities. However, directly deploying LLMs for FSA applications incurs high
+inference costs. Therefore, this paper investigates the distillation of
+fine-grained sentiment understanding from LLMs into small language models
+(SLMs). We prompt LLMs to examine and interpret the sentiments of given reviews
+and then utilize the generated content to pretrain SLMs. Additionally, we
+develop a comprehensive FSA benchmark to evaluate both SLMs and LLMs. Extensive
+experiments on this benchmark reveal that: (1) distillation significantly
+enhances the performance of SLMs in FSA tasks, achieving a 6.00\% improvement
+in $F_1$-score, and the distilled model can outperform Llama-2-7b with only
+220M parameters; (2) distillation equips SLMs with excellent zero-shot
+sentiment classification capabilities, enabling them to match or even exceed
+their teacher models. These results suggest that distillation from LLMs is a
+highly promising direction for FSA. We will release our code, data, and
+pretrained model weights at https://github.com/HITSZ-HLT/FSA-Distillation.
+
+
+
+
+ + ♻ ☆ Geometric-Averaged Preference Optimization for Soft Preference Labels NeurIPS 2024 +
+
+
+
+
+
+
+
+ Hiroki Furuta, Kuang-Huei Lee, Shixiang Shane Gu, Yutaka Matsuo, Aleksandra Faust, Heiga Zen, Izzeddin Gur
+
+
+ Many algorithms for aligning LLMs with human preferences assume that human
+preferences are binary and deterministic. However, human preferences can vary
+across individuals, and therefore should be represented distributionally. In
+this work, we introduce the distributional soft preference labels and improve
+Direct Preference Optimization (DPO) with a weighted geometric average of the
+LLM output likelihood in the loss function. This approach adjusts the scale of
+learning loss based on the soft labels such that the loss would approach zero
+when the responses are closer to equally preferred. This simple modification
+can be easily applied to any DPO-based methods and mitigate over-optimization
+and objective mismatch, which prior works suffer from. Our experiments simulate
+the soft preference labels with AI feedback from LLMs and demonstrate that
+geometric averaging consistently improves performance on standard benchmarks
+for alignment research. In particular, we observe more preferable responses
+than binary labels and significant improvements where modestly-confident labels
+are in the majority.
+
+
+
+ comment: Accepted at NeurIPS 2024
+
+
+
+ + ♻ ☆ GFG -- Gender-Fair Generation: A CALAMITA Challenge +
+
+
+
+
+
+
+
+ Simona Frenda, Andrea Piergentili, Beatrice Savoldi, Marco Madeddu, Martina Rosola, Silvia Casola, Chiara Ferrando, Viviana Patti, Matteo Negri, Luisa Bentivogli
+
+
+ Gender-fair language aims at promoting gender equality by using terms and
+expressions that include all identities and avoid reinforcing gender
+stereotypes. Implementing gender-fair strategies is particularly challenging in
+heavily gender-marked languages, such as Italian. To address this, the
+Gender-Fair Generation challenge intends to help shift toward gender-fair
+language in written communication. The challenge, designed to assess and
+monitor the recognition and generation of gender-fair language in both mono-
+and cross-lingual scenarios, includes three tasks: (1) the detection of
+gendered expressions in Italian sentences, (2) the reformulation of gendered
+expressions into gender-fair alternatives, and (3) the generation of
+gender-fair language in automatic translation from English to Italian. The
+challenge relies on three different annotated datasets: the GFL-it corpus,
+which contains Italian texts extracted from administrative documents provided
+by the University of Brescia; GeNTE, a bilingual test set for gender-neutral
+rewriting and translation built upon a subset of the Europarl dataset; and
+Neo-GATE, a bilingual test set designed to assess the use of non-binary
+neomorphemes in Italian for both fair formulation and translation tasks.
+Finally, each task is evaluated with specific metrics: average of F1-score
+obtained by means of BERTScore computed on each entry of the datasets for task
+1, an accuracy measured with a gender-neutral classifier, and a
+coverage-weighted accuracy for tasks 2 and 3.
+
+
+
+ comment: To refer to this paper please cite the CEUR-ws publication available
+ at https://ceur-ws.org/Vol-3878/
+
+
+
+ + ♻ ☆ A Simple Recipe for Contrastively Pre-training Video-First Encoders + Beyond 16 Frames +
+
+
+
+
+
+
+
+ Pinelopi Papalampidi, Skanda Koppula, Shreya Pathak, Justin Chiu, Joe Heyward, Viorica Patraucean, Jiajun Shen, Antoine Miech, Andrew Zisserman, Aida Nematzadeh
+
+
+ Understanding long, real-world videos requires modeling of long-range visual
+dependencies. To this end, we explore video-first architectures, building on
+the common paradigm of transferring large-scale, image--text models to video
+via shallow temporal fusion. However, we expose two limitations to the
+approach: (1) decreased spatial capabilities, likely due to poor
+video--language alignment in standard video datasets, and (2) higher memory
+consumption, bottlenecking the number of frames that can be processed. To
+mitigate the memory bottleneck, we systematically analyze the memory/accuracy
+trade-off of various efficient methods: factorized attention,
+parameter-efficient image-to-video adaptation, input masking, and
+multi-resolution patchification. Surprisingly, simply masking large portions of
+the video (up to 75%) during contrastive pre-training proves to be one of the
+most robust ways to scale encoders to videos up to 4.3 minutes at 1 FPS. Our
+simple approach for training long video-to-text models, which scales to 1B
+parameters, does not add new architectural complexity and is able to outperform
+the popular paradigm of using much larger LLMs as an information aggregator
+over segment-based information on benchmarks with long-range temporal
+dependencies (YouCook2, EgoSchema).
+
+
+
+
+ + ♻ ☆ InfAlign: Inference-aware language model alignment +
+
+
+
+
+
+
+
+ Ananth Balashankar, Ziteng Sun, Jonathan Berant, Jacob Eisenstein, Michael Collins, Adrian Hutter, Jong Lee, Chirag Nagpal, Flavien Prost, Aradhana Sinha, Ananda Theertha Suresh, Ahmad Beirami
+
+
+ Language model alignment has become a critical step in training modern
+generative language models. The goal of alignment is to finetune a reference
+model such that the win rate of a sample from the aligned model over a sample
+from the reference model is high, subject to a KL divergence constraint. Today,
+we are increasingly using inference-time algorithms (e.g., Best-of-N,
+controlled decoding, tree search) to decode from language models rather than
+standard sampling. However, the alignment objective does not capture such
+inference-time decoding procedures. We show that the existing alignment
+framework is sub-optimal in view of such inference-time methods. We then modify
+the alignment objective and propose a framework for inference-aware alignment
+(IAPO). We prove that for any inference-time decoding algorithm, the optimal
+solution that optimizes the inference-time win rate of the aligned policy
+against the reference policy is the solution to the typical RLHF problem with a
+transformation of the reward. This motivates us to provide the KL-regularized
+calibrate-and-transform RL (CTRL) algorithm to solve this problem, which
+involves a reward calibration step and a KL-regularized reward maximization
+step with a transformation of the calibrated reward. We particularize our study
+to two important inference-time strategies: best-of-N sampling and best-of-N
+jailbreaking, where N responses are sampled from the model and the one with the
+highest or lowest reward is selected. We propose specific transformations for
+these strategies and demonstrate that our framework offers significant
+improvements over existing state-of-the-art methods for language model
+alignment. Empirically, we outperform baselines that are designed without
+taking inference-time decoding into consideration by 8-12% and 4-9% on
+inference-time win rates over the Anthropic helpfulness and harmlessness dialog
+benchmark datasets.
+
+
+
+
+ + ♻ ☆ LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM + Evaluation +
+ +
+ We introduce LLM-as-an-Interviewer, a novel paradigm for evaluating large
+language models (LLMs). This approach leverages multi-turn interactions where
+the LLM interviewer actively provides feedback on responses and poses follow-up
+questions to the evaluated LLM. At the start of the interview, the LLM
+interviewer dynamically modifies datasets to generate initial questions,
+mitigating data contamination. We apply the LLM-as-an-Interviewer framework to
+evaluate six models on the MATH and DepthQA tasks. Our results show that the
+framework effectively provides insights into LLM performance, including the
+quality of initial responses, adaptability to feedback, and ability to address
+follow-up queries like clarification or additional knowledge requests. The
+framework also addresses key limitations of conventional methods like
+LLM-as-a-Judge, including verbosity bias and inconsistency across runs.
+Finally, we propose the Interview Report, which aggregates insights from the
+interview process, providing examples and a comprehensive analysis of the LLM's
+strengths and weaknesses. This report offers a detailed snapshot of the model's
+real-world applicability. The code for our framework is publicly available at
+https://github.com/interview-eval/.
+
+
+
+
+ + ♻ ☆ LLM-based Translation Inference with Iterative Bilingual Understanding +
+
+
+
+
+
+
+
+ Andong Chen, Kehai Chen, Yang Xiang, Xuefeng Bai, Muyun Yang, Yang Feng, Tiejun Zhao, Min zhang
+
+
+ The remarkable understanding and generation capabilities of large language
+models (LLMs) have greatly improved translation performance. However, incorrect
+understanding of the sentence to be translated can degrade translation quality.
+To address this issue, we proposed a novel Iterative Bilingual Understanding
+Translation (IBUT) method based on the cross-lingual capabilities of LLMs and
+the dual characteristics of translation tasks. The cross-lingual capability of
+LLMs enables the generation of contextual understanding for both the source and
+target languages separately. Furthermore, the dual characteristics allow IBUT
+to generate effective cross-lingual feedback, iteratively refining contextual
+understanding, thereby reducing errors and improving translation performance.
+Experimental results showed that the proposed IBUT outperforms several strong
+comparison methods, especially being generalized to multiple domains (e.g.,
+news, commonsense, and cultural translation benchmarks).
+
+
+
+ comment: Work in progress
+
+
+
+ + ♻ ☆ LLM-jp: A Cross-organizational Project for the Research and Development + of Fully Open Japanese LLMs +
+
+
+
+
+
+
+
+ LLM-jp, :, Akiko Aizawa, Eiji Aramaki, Bowen Chen, Fei Cheng, Hiroyuki Deguchi, Rintaro Enomoto, Kazuki Fujii, Kensuke Fukumoto, Takuya Fukushima, Namgi Han, Yuto Harada, Chikara Hashimoto, Tatsuya Hiraoka, Shohei Hisada, Sosuke Hosokawa, Lu Jie, Keisuke Kamata, Teruhito Kanazawa, Hiroki Kanezashi, Hiroshi Kataoka, Satoru Katsumata, Daisuke Kawahara, Seiya Kawano, Atsushi Keyaki, Keisuke Kiryu, Hirokazu Kiyomaru, Takashi Kodama, Takahiro Kubo, Yohei Kuga, Ryoma Kumon, Shuhei Kurita, Sadao Kurohashi, Conglong Li, Taiki Maekawa, Hiroshi Matsuda, Yusuke Miyao, Kentaro Mizuki, Sakae Mizuki, Yugo Murawaki, Akim Mousterou, Ryo Nakamura, Taishi Nakamura, Kouta Nakayama, Tomoka Nakazato, Takuro Niitsuma, Jiro Nishitoba, Yusuke Oda, Hayato Ogawa, Takumi Okamoto, Naoaki Okazaki, Yohei Oseki, Shintaro Ozaki, Koki Ryu, Rafal Rzepka, Keisuke Sakaguchi, Shota Sasaki, Satoshi Sekine, Kohei Suda, Saku Sugawara, Issa Sugiura, Hiroaki Sugiyama, Hisami Suzuki, Jun Suzuki, Toyotaro Suzumura, Kensuke Tachibana, Yu Takagi, Kyosuke Takami, Koichi Takeda, Masashi Takeshita, Masahiro Tanaka, Kenjiro Taura, Arseny Tolmachev, Nobuhiro Ueda, Zhen Wan, Shuntaro Yada, Sakiko Yahata, Yuya Yamamoto, Yusuke Yamauchi, Hitomi Yanaka, Rio Yokota, Koichiro Yoshino
+
+
+ This paper introduces LLM-jp, a cross-organizational project for the research
+and development of Japanese large language models (LLMs). LLM-jp aims to
+develop open-source and strong Japanese LLMs, and as of this writing, more than
+1,500 participants from academia and industry are working together for this
+purpose. This paper presents the background of the establishment of LLM-jp,
+summaries of its activities, and technical reports on the LLMs developed by
+LLM-jp. For the latest activities, visit https://llm-jp.nii.ac.jp/en/.
+
+
+
+
+ + ♻ ☆ Adaptive Prompting for Continual Relation Extraction: A Within-Task + Variance Perspective AAAI 2025 +
+
+
+
+
+
+
+
+ Minh Le, Tien Ngoc Luu, An Nguyen The, Thanh-Thien Le, Trang Nguyen, Tung Thanh Nguyen, Linh Ngo Van, Thien Huu Nguyen
+
+
+ To address catastrophic forgetting in Continual Relation Extraction (CRE),
+many current approaches rely on memory buffers to rehearse previously learned
+knowledge while acquiring new tasks. Recently, prompt-based methods have
+emerged as potent alternatives to rehearsal-based strategies, demonstrating
+strong empirical performance. However, upon analyzing existing prompt-based
+approaches for CRE, we identified several critical limitations, such as
+inaccurate prompt selection, inadequate mechanisms for mitigating forgetting in
+shared parameters, and suboptimal handling of cross-task and within-task
+variances. To overcome these challenges, we draw inspiration from the
+relationship between prefix-tuning and mixture of experts, proposing a novel
+approach that employs a prompt pool for each task, capturing variations within
+each task while enhancing cross-task variances. Furthermore, we incorporate a
+generative model to consolidate prior knowledge within shared parameters,
+eliminating the need for explicit data storage. Extensive experiments validate
+the efficacy of our approach, demonstrating superior performance over
+state-of-the-art prompt-based and rehearsal-free methods in continual relation
+extraction.
+
+
+
+ comment: Accepted to AAAI 2025
+
+
+
+ + ♻ ☆ A Modular-based Strategy for Mitigating Gradient Conflicts in + Simultaneous Speech Translation ICASSP 2025 +
+
+
+
+
+
+
+
+ Xiaoqian Liu, Yangfan Du, Jianjin Wang, Yuan Ge, Chen Xu, Tong Xiao, Guocheng Chen, Jingbo Zhu
+
+
+ Simultaneous Speech Translation (SimulST) involves generating target language
+text while continuously processing streaming speech input, presenting
+significant real-time challenges. Multi-task learning is often employed to
+enhance SimulST performance but introduces optimization conflicts between
+primary and auxiliary tasks, potentially compromising overall efficiency. The
+existing model-level conflict resolution methods are not well-suited for this
+task which exacerbates inefficiencies and leads to high GPU memory consumption.
+To address these challenges, we propose a Modular Gradient Conflict Mitigation
+(MGCM) strategy that detects conflicts at a finer-grained modular level and
+resolves them utilizing gradient projection. Experimental results demonstrate
+that MGCM significantly improves SimulST performance, particularly under medium
+and high latency conditions, achieving a 0.68 BLEU score gain in offline tasks.
+Additionally, MGCM reduces GPU memory consumption by over 95\% compared to
+other conflict mitigation methods, establishing it as a robust solution for
+SimulST tasks.
+
+
+
+ comment: Accepted to ICASSP 2025
+
+
+
+ + ♻ ☆ Align Anything: Training All-Modality Models to Follow Instructions with + Language Feedback +
+
+
+
+
+
+
+
+ Jiaming Ji, Jiayi Zhou, Hantao Lou, Boyuan Chen, Donghai Hong, Xuyao Wang, Wenqi Chen, Kaile Wang, Rui Pan, Jiahao Li, Mohan Wang, Josef Dai, Tianyi Qiu, Hua Xu, Dong Li, Weipeng Chen, Jun Song, Bo Zheng, Yaodong Yang
+
+
+ Reinforcement learning from human feedback (RLHF) has proven effective in
+enhancing the instruction-following capabilities of large language models;
+however, it remains underexplored in the cross-modality domain. As the number
+of modalities increases, aligning all-modality models with human intentions --
+such as instruction following -- becomes a pressing challenge. In this work, we
+make the first attempt to fine-tune all-modality models (i.e. input and output
+with any modality, also named any-to-any models) using human preference data
+across all modalities (including text, image, audio, and video), ensuring its
+behavior aligns with human intentions. This endeavor presents several
+challenges. First, there is no large-scale all-modality human preference data
+in existing open-source resources, as most datasets are limited to specific
+modalities, predominantly text and image. Secondly, the effectiveness of binary
+preferences in RLHF for post-training alignment in complex all-modality
+scenarios remains an unexplored area. Finally, there is a lack of a systematic
+framework to evaluate the capabilities of all-modality models, particularly
+regarding modality selection and synergy. To address these challenges, we
+propose the align-anything framework, which includes meticulously annotated
+200k all-modality human preference data. Then, we introduce an alignment method
+that learns from unified language feedback, effectively capturing complex
+modality-specific human preferences and enhancing the model's
+instruction-following capabilities. Furthermore, to assess performance
+improvements in all-modality models after post-training alignment, we construct
+a challenging all-modality capability evaluation framework -- eval-anything.
+All data, models, and code frameworks have been open-sourced for the community.
+For more details, please refer to
+https://github.com/PKU-Alignment/align-anything.
+
+
+
+
+ + ♻ ☆ Large Language Models for Classical Chinese Poetry Translation: + Benchmarking, Evaluating, and Improving +
+
+
+
+
+
+
+
+ Andong Chen, Lianzhang Lou, Kehai Chen, Xuefeng Bai, Yang Xiang, Muyun Yang, Tiejun Zhao, Min Zhang
+
+
+ Different from the traditional translation tasks, classical Chinese poetry
+translation requires both adequacy and fluency in translating culturally and
+historically significant content and linguistic poetic elegance. Large language
+models (LLMs) with impressive multilingual capabilities may bring a ray of hope
+to achieve this extreme translation demand. This paper first introduces a
+suitable benchmark (PoetMT) where each Chinese poetry has a recognized elegant
+translation. Meanwhile, we propose a new metric based on GPT-4 to evaluate the
+extent to which current LLMs can meet these demands. Our empirical evaluation
+reveals that the existing LLMs fall short in the challenging task. Hence, we
+propose a Retrieval-Augmented Machine Translation (RAT) method which
+incorporates knowledge related to classical poetry for advancing the
+translation of Chinese Poetry in LLMs. Experimental results show that RAT
+consistently outperforms all comparison methods regarding wildly used BLEU,
+COMET, BLEURT, our proposed metric, and human evaluation.
+
+
+
+ comment: Work in progress
+
+
+
+ + ♻ ☆ ConTrans: Weak-to-Strong Alignment Engineering via Concept + Transplantation +
+ +
+ Ensuring large language models (LLM) behave consistently with human goals,
+values, and intentions is crucial for their safety but yet computationally
+expensive. To reduce the computational cost of alignment training of LLMs,
+especially for those with a huge number of parameters, and to reutilize learned
+value alignment, we propose ConTrans, a novel framework that enables
+weak-to-strong alignment transfer via concept transplantation. From the
+perspective of representation engineering, ConTrans refines concept vectors in
+value alignment from a source LLM (usually a weak yet aligned LLM). The refined
+concept vectors are then reformulated to adapt to the target LLM (usually a
+strong yet unaligned base LLM) via affine transformation. In the third step,
+ConTrans transplants the reformulated concept vectors into the residual stream
+of the target LLM. Experiments demonstrate the successful transplantation of a
+wide range of aligned concepts from 7B models to 13B and 70B models across
+multiple LLMs and LLM families. Remarkably, ConTrans even surpasses
+instruction-tuned models in terms of truthfulness. Experiment results validate
+the effectiveness of both inter-LLM-family and intra-LLM-family concept
+transplantation. Our work successfully demonstrates an alternative way to
+achieve weak-to-strong alignment generalization and control.
+
+
+
+
+ + ♻ ☆ Large Language Model-Brained GUI Agents: A Survey +
+
+
+
+
+
+
+
+ Chaoyun Zhang, Shilin He, Jiaxu Qian, Bowen Li, Liqun Li, Si Qin, Yu Kang, Minghua Ma, Guyue Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
+
+
+ GUIs have long been central to human-computer interaction, providing an
+intuitive and visually-driven way to access and interact with digital systems.
+The advent of LLMs, particularly multimodal models, has ushered in a new era of
+GUI automation. They have demonstrated exceptional capabilities in natural
+language understanding, code generation, and visual processing. This has paved
+the way for a new generation of LLM-brained GUI agents capable of interpreting
+complex GUI elements and autonomously executing actions based on natural
+language instructions. These agents represent a paradigm shift, enabling users
+to perform intricate, multi-step tasks through simple conversational commands.
+Their applications span across web navigation, mobile app interactions, and
+desktop automation, offering a transformative user experience that
+revolutionizes how individuals interact with software. This emerging field is
+rapidly advancing, with significant progress in both research and industry.
+ To provide a structured understanding of this trend, this paper presents a
+comprehensive survey of LLM-brained GUI agents, exploring their historical
+evolution, core components, and advanced techniques. We address research
+questions such as existing GUI agent frameworks, the collection and utilization
+of data for training specialized GUI agents, the development of large action
+models tailored for GUI tasks, and the evaluation metrics and benchmarks
+necessary to assess their effectiveness. Additionally, we examine emerging
+applications powered by these agents. Through a detailed analysis, this survey
+identifies key research gaps and outlines a roadmap for future advancements in
+the field. By consolidating foundational knowledge and state-of-the-art
+developments, this work aims to guide both researchers and practitioners in
+overcoming challenges and unlocking the full potential of LLM-brained GUI
+agents.
+
+
+
+ comment: The collection of papers reviewed in this survey will be hosted and
+ regularly updated on the GitHub repository:
+ https://github.com/vyokky/LLM-Brained-GUI-Agents-Survey Additionally, a
+ searchable webpage is available at https://aka.ms/gui-agent for easier access
+ and exploration
+
+
+
+ + ♻ ☆ Yi: Open Foundation Models by 01.AI +
+
+
+
+
+
+
+
+ 01. AI, :, Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, Zonghong Dai
+
+
+ We introduce the Yi model family, a series of language and multimodal models
+that demonstrate strong multi-dimensional capabilities. The Yi model family is
+based on 6B and 34B pretrained language models, then we extend them to chat
+models, 200K long context models, depth-upscaled models, and vision-language
+models. Our base models achieve strong performance on a wide range of
+benchmarks like MMLU, and our finetuned chat models deliver strong human
+preference rate on major evaluation platforms like AlpacaEval and Chatbot
+Arena. Building upon our scalable super-computing infrastructure and the
+classical transformer architecture, we attribute the performance of Yi models
+primarily to its data quality resulting from our data-engineering efforts. For
+pretraining, we construct 3.1 trillion tokens of English and Chinese corpora
+using a cascaded data deduplication and quality filtering pipeline. For
+finetuning, we polish a small scale (less than 10K) instruction dataset over
+multiple iterations such that every single instance has been verified directly
+by our machine learning engineers. For vision-language, we combine the chat
+language model with a vision transformer encoder and train the model to align
+visual representations to the semantic space of the language model. We further
+extend the context length to 200K through lightweight continual pretraining and
+demonstrate strong needle-in-a-haystack retrieval performance. We show that
+extending the depth of the pretrained checkpoint through continual pretraining
+further improves performance. We believe that given our current results,
+continuing to scale up model parameters using thoroughly optimized data will
+lead to even stronger frontier models.
+
+
+
+
+ + ♻ ☆ EHRCon: Dataset for Checking Consistency between Unstructured Notes and + Structured Tables in Electronic Health Records +
+
+
+
+
+
+
+
+ Yeonsu Kwon, Jiho Kim, Gyubok Lee, Seongsu Bae, Daeun Kyung, Wonchul Cha, Tom Pollard, Alistair Johnson, Edward Choi
+
+
+ Electronic Health Records (EHRs) are integral for storing comprehensive
+patient medical records, combining structured data (e.g., medications) with
+detailed clinical notes (e.g., physician notes). These elements are essential
+for straightforward data retrieval and provide deep, contextual insights into
+patient care. However, they often suffer from discrepancies due to unintuitive
+EHR system designs and human errors, posing serious risks to patient safety. To
+address this, we developed EHRCon, a new dataset and task specifically designed
+to ensure data consistency between structured tables and unstructured notes in
+EHRs. EHRCon was crafted in collaboration with healthcare professionals using
+the MIMIC-III EHR dataset, and includes manual annotations of 4,101 entities
+across 105 clinical notes checked against database entries for consistency.
+EHRCon has two versions, one using the original MIMIC-III schema, and another
+using the OMOP CDM schema, in order to increase its applicability and
+generalizability. Furthermore, leveraging the capabilities of large language
+models, we introduce CheckEHR, a novel framework for verifying the consistency
+between clinical notes and database tables. CheckEHR utilizes an eight-stage
+process and shows promising results in both few-shot and zero-shot settings.
+The code is available at https://github.com/dustn1259/EHRCon.
+
+
+
+
+ + ♻ ☆ Aligning the Objective of LLM-based Program Repair ICSE'25 +
+ +
+ Large language models (LLMs) have achieved decent results on automated
+program repair (APR). However, the next token prediction training objective of
+decoder-only LLMs (e.g., GPT-4) is misaligned with the masked span prediction
+objective of current infilling-style methods, which impedes LLMs from fully
+leveraging pre-trained knowledge for program repair. In addition, while some
+LLMs can locate and repair bugs in certain functions using the related
+artifacts (e.g., test cases), existing methods still depend on statement-level
+fault localization methods to provide a list of buggy hunks for repair. This
+restriction hinders LLMs from exploring potential patches beyond the given
+locations.
+ In this paper, we investigate a new approach to adapt LLMs to program repair.
+Our core insight is that LLM's APR capability can be greatly improved by simply
+aligning the output to their training objective and allowing them to refine the
+whole program without first identifying faulty statements. Based on this
+insight, we designed D4C, a straightforward prompting framework for APR. D4C
+can repair 180 bugs correctly in Defects4J, with each patch being sampled only
+10 times. This surpasses the SOTA APR methods with perfect fault localization
+by 10% and reduces the patch sampling number by 90%. Our findings reveal that
+(1) objective alignment is crucial for fully exploiting LLM's pre-trained
+capability, and (2) replacing the traditional localize-buggy-hunks-then-repair
+workflow with direct debugging is more effective for LLM-based APR methods.
+Thus, we believe this paper introduces a new mindset for harnessing LLMs in
+APR.
+
+
+
+ comment: Accepted by ICSE'25
+
+
+
+ + ♻ ☆ ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image + Captioning AAAI 2025 +
+ +
+ Recent lightweight image captioning models using retrieved data mainly focus
+on text prompts. However, previous works only utilize the retrieved text as
+text prompts, and the visual information relies only on the CLIP visual
+embedding. Because of this issue, there is a limitation that the image
+descriptions inherent in the prompt are not sufficiently reflected in the
+visual embedding space. To tackle this issue, we propose ViPCap, a novel
+retrieval text-based visual prompt for lightweight image captioning. ViPCap
+leverages the retrieved text with image information as visual prompts to
+enhance the ability of the model to capture relevant visual information. By
+mapping text prompts into the CLIP space and generating multiple randomized
+Gaussian distributions, our method leverages sampling to explore randomly
+augmented distributions and effectively retrieves the semantic features that
+contain image information. These retrieved features are integrated into the
+image and designated as the visual prompt, leading to performance improvements
+on the datasets such as COCO, Flickr30k, and NoCaps. Experimental results
+demonstrate that ViPCap significantly outperforms prior lightweight captioning
+models in efficiency and effectiveness, demonstrating the potential for a
+plug-and-play solution.
+
+
+
+ comment: Accepted to AAAI 2025
+
+
+
+ + ♻ ☆ SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language + Models +
+ +
+ Large language models (LLMs) have seen widespread adoption due to their
+remarkable performance across various applications, driving the accelerated
+development of a large number of diverse LLMs. However, these individual LLMs
+show limitations in generalization and performance on complex tasks due to
+inherent training biases, model size constraints, and the quality or diversity
+of pre-training datasets. A promising direction is to efficiently harness the
+diverse capabilities of LLMs to overcome these individual limitations. To
+address these limitations, we introduce a novel LLM selection algorithm called
+SelectLLM, which efficiently directs input queries to the most suitable subset
+of LLMs from a large pool, ensuring that the selected models collectively
+provide accurate responses. SelectLLM employs a multi-label classifier and
+policy based on the classifier's predictions and confidence scores in selecting
+an optimal, query-aware, and lightweight subset of LLMs. Our findings indicate
+that the proposed model outperforms existing ensemble-based baselines and
+achieves competitive performance with similarly sized top-performing LLMs while
+maintaining efficiency. Specifically, it achieves a huge reduction in inference
+latency on two challenging reasoning benchmarks: 13% on GSM8K and 70% on MMLU,
+compared to the top-performing baselines. Also, we establish a theoretical
+upper bound by an oracle with LLMs and explore in-depth linguistic analysis to
+understand the performance gap between Oracle and SelectLLM.
+
+
+
+
+ + ♻ ☆ Automated Review Generation Method Based on Large Language Models +
+
+
+
+
+
+
+
+ Shican Wu, Xiao Ma, Dehui Luo, Lulu Li, Xiangcheng Shi, Xin Chang, Xiaoyun Lin, Ran Luo, Chunlei Pei, Changyin Du, Zhi-Jian Zhao, Jinlong Gong
+
+
+ Literature research, vital for scientific work, faces the challenge of
+surging information volumes exceeding researchers' processing capabilities. We
+present an automated review generation method based on large language models
+(LLMs) to overcome efficiency bottlenecks and reduce cognitive load. Our
+statistically validated evaluation framework demonstrates that the generated
+reviews match or exceed manual quality, offering broad applicability across
+research fields without requiring users' domain knowledge. Applied to propane
+dehydrogenation (PDH) catalysts, our method swiftly analyzed 343 articles,
+averaging seconds per article per LLM account, producing comprehensive reviews
+spanning 35 topics, with extended analysis of 1041 articles providing insights
+into catalysts' properties. Through multi-layered quality control, we
+effectively mitigated LLMs' hallucinations, with expert verification confirming
+accuracy and citation integrity while demonstrating hallucination risks reduced
+to below 0.5\% with 95\% confidence. Released Windows application enables
+one-click review generation, enhancing research productivity and literature
+recommendation efficiency while setting the stage for broader scientific
+explorations.
+
+
+
+ comment: 21 pages, 5 figures, 1 tables Code:
+ https://github.com/TJU-ECAT-AI/AutomaticReviewGeneration Data:
+ https://github.com/TJU-ECAT-AI/AutomaticReviewGenerationData This research
+ has been invited for a Short Oral presentation at the 18th ICC -
+ International Congress on Catalysis, taking place in Lyon, France from July
+ 14-19, 2024
+
+
+
+ + ♻ ☆ In-Context Learning with Iterative Demonstration Selection +
+ +
+ Spurred by advancements in scale, large language models (LLMs) have
+demonstrated strong few-shot learning ability via in-context learning (ICL).
+However, the performance of ICL has been shown to be highly sensitive to the
+selection of few-shot demonstrations. Selecting the most suitable examples as
+context remains an ongoing challenge and an open problem. Existing literature
+has highlighted the importance of selecting examples that are diverse or
+semantically similar to the test sample while ignoring the fact that the
+optimal selection dimension, i.e., diversity or similarity, is task-specific.
+Based on how the test sample is answered, we propose Iterative Demonstration
+Selection (IDS) to leverage the merits of both dimensions. Using zero-shot
+chain-of-thought reasoning (Zero-shot-CoT), IDS iteratively selects examples
+that are diverse but still strongly correlated with the test sample as ICL
+demonstrations. Specifically, IDS applies Zero-shot-CoT to the test sample
+before demonstration selection. The output reasoning path is then used to
+choose demonstrations that are prepended to the test sample for inference. The
+generated answer is followed by its corresponding reasoning path for extracting
+a new set of demonstrations in the next iteration. After several iterations,
+IDS adopts majority voting to obtain the final result. Through extensive
+experiments on tasks including reasoning, question answering, and topic
+classification, we demonstrate that IDS can consistently outperform existing
+ICL demonstration selection methods.
+
+
+
+
+ + ♻ ☆ Memorization Over Reasoning? Exposing and Mitigating Verbatim + Memorization in Large Language Models' Character Understanding Evaluation +
+ +
+ Recently, Large Language Models (LLMs) have shown impressive performance in
+character understanding tasks, such as analyzing the roles, personalities, and
+relationships of fictional characters. However, the extensive pre-training
+corpora used by LLMs raise concerns that they may rely on memorizing popular
+fictional works rather than genuinely understanding and reasoning about them.
+In this work, we argue that 'gist memory'-capturing essential meaning - should
+be the primary mechanism for character understanding tasks, as opposed to
+'verbatim memory' - exact match of a string. We introduce a simple yet
+effective method to mitigate mechanized memorization in character understanding
+evaluations while preserving the essential implicit cues needed for
+comprehension and reasoning. Our approach reduces memorization-driven
+performance on popular fictional works from 96% accuracy to 72% and results in
+up to an 18% drop in accuracy across various character understanding tasks.
+These findings underscore the issue of data contamination in existing
+benchmarks, which often measure memorization rather than true character
+understanding.
+
+
+
+
+ + ♻ ☆ Augmenting Biomedical Named Entity Recognition with General-domain + Resources +
+
+
+
+
+
+
+
+ Yu Yin, Hyunjae Kim, Xiao Xiao, Chih Hsuan Wei, Jaewoo Kang, Zhiyong Lu, Hua Xu, Meng Fang, Qingyu Chen
+
+
+ Training a neural network-based biomedical named entity recognition (BioNER)
+model usually requires extensive and costly human annotations. While several
+studies have employed multi-task learning with multiple BioNER datasets to
+reduce human effort, this approach does not consistently yield performance
+improvements and may introduce label ambiguity in different biomedical corpora.
+We aim to tackle those challenges through transfer learning from easily
+accessible resources with fewer concept overlaps with biomedical datasets. We
+proposed GERBERA, a simple-yet-effective method that utilized general-domain
+NER datasets for training. We performed multi-task learning to train a
+pre-trained biomedical language model with both the target BioNER dataset and
+the general-domain dataset. Subsequently, we fine-tuned the models specifically
+for the BioNER dataset. We systematically evaluated GERBERA on five datasets of
+eight entity types, collectively consisting of 81,410 instances. Despite using
+fewer biomedical resources, our models demonstrated superior performance
+compared to baseline models trained with additional BioNER datasets.
+Specifically, our models consistently outperformed the baseline models in six
+out of eight entity types, achieving an average improvement of 0.9% over the
+best baseline performance across eight entities. Our method was especially
+effective in amplifying performance on BioNER datasets characterized by limited
+data, with a 4.7% improvement in F1 scores on the JNLPBA-RNA dataset. This
+study introduces a new training method that leverages cost-effective
+general-domain NER datasets to augment BioNER models. This approach
+significantly improves BioNER model performance, making it a valuable asset for
+scenarios with scarce or costly biomedical datasets.
+
+
+
+ comment: Published in JBI 2024. We make data, codes, and models publicly
+ available via https://github.com/qingyu-qc/bioner_gerbera
+
+
+
+ + ♻ ☆ Next Token Prediction Towards Multimodal Intelligence: A Comprehensive + Survey +
+
+
+
+
+
+
+
+ Liang Chen, Zekun Wang, Shuhuai Ren, Lei Li, Haozhe Zhao, Yunshui Li, Zefan Cai, Hongcheng Guo, Lei Zhang, Yizhe Xiong, Yichi Zhang, Ruoyu Wu, Qingxiu Dong, Ge Zhang, Jian Yang, Lingwei Meng, Shujie Hu, Yulong Chen, Junyang Lin, Shuai Bai, Andreas Vlachos, Xu Tan, Minjia Zhang, Wen Xiao, Aaron Yee, Tianyu Liu, Baobao Chang
+
+
+ Building on the foundations of language modeling in natural language
+processing, Next Token Prediction (NTP) has evolved into a versatile training
+objective for machine learning tasks across various modalities, achieving
+considerable success. As Large Language Models (LLMs) have advanced to unify
+understanding and generation tasks within the textual modality, recent research
+has shown that tasks from different modalities can also be effectively
+encapsulated within the NTP framework, transforming the multimodal information
+into tokens and predict the next one given the context. This survey introduces
+a comprehensive taxonomy that unifies both understanding and generation within
+multimodal learning through the lens of NTP. The proposed taxonomy covers five
+key aspects: Multimodal tokenization, MMNTP model architectures, unified task
+representation, datasets \& evaluation, and open challenges. This new taxonomy
+aims to aid researchers in their exploration of multimodal intelligence. An
+associated GitHub repository collecting the latest papers and repos is
+available at https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction
+
+
+
+ comment: 69 papes, 18 figures, repo at
+ https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction
+
+
+
+ + ♻ ☆ Large-scale moral machine experiment on large language models +
+ +
+ The rapid advancement of Large Language Models (LLMs) and their potential
+integration into autonomous driving systems necessitates understanding their
+moral decision-making capabilities. While our previous study examined four
+prominent LLMs using the Moral Machine experimental framework, the dynamic
+landscape of LLM development demands a more comprehensive analysis. Here, we
+evaluate moral judgments across 52 different LLMs, including multiple versions
+of proprietary models (GPT, Claude, Gemini) and open-source alternatives
+(Llama, Gemma), to assess their alignment with human moral preferences in
+autonomous driving scenarios. Using a conjoint analysis framework, we evaluated
+how closely LLM responses aligned with human preferences in ethical dilemmas
+and examined the effects of model size, updates, and architecture. Results
+showed that proprietary models and open-source models exceeding 10 billion
+parameters demonstrated relatively close alignment with human judgments, with a
+significant negative correlation between model size and distance from human
+judgments in open-source models. However, model updates did not consistently
+improve alignment with human preferences, and many LLMs showed excessive
+emphasis on specific ethical principles. These findings suggest that while
+increasing model size may naturally lead to more human-like moral judgments,
+practical implementation in autonomous driving systems requires careful
+consideration of the trade-off between judgment quality and computational
+efficiency. Our comprehensive analysis provides crucial insights for the
+ethical design of autonomous systems and highlights the importance of
+considering cultural contexts in AI moral decision-making.
+
+
+
+ comment: 21 pages, 6 figures
+
+
+
+ + ♻ ☆ Understanding Synthetic Context Extension via Retrieval Heads +
+ +
+ Long-context LLMs are increasingly in demand for applications such as
+retrieval-augmented generation. To defray the cost of pretraining LLMs over
+long contexts, recent work takes an approach of synthetic context extension:
+fine-tuning LLMs with synthetically generated long-context data in a
+post-training stage. However, it remains unclear how and why this synthetic
+context extension imparts abilities for downstream long-context tasks. In this
+paper, we investigate fine-tuning on synthetic data for three long-context
+tasks that require retrieval and reasoning. We vary the realism of "needle"
+concepts to be retrieved and diversity of the surrounding "haystack" context,
+from using LLMs to construct synthetic documents to using templated relations
+and creating symbolic datasets. We find that models trained on synthetic data
+fall short of the real data, but surprisingly, the mismatch can be interpreted
+and even predicted in terms of a special set of attention heads that are
+responsible for retrieval over long context, retrieval heads (Wu et al., 2024).
+The retrieval heads learned on synthetic data have high overlap with retrieval
+heads learned on real data, and there is a strong correlation between the
+recall of heads learned and the downstream performance of a model. Furthermore,
+with attention knockout and activation patching, we mechanistically show that
+retrieval heads are necessary and explain model performance, although they are
+not totally sufficient. Our results shed light on how to interpret synthetic
+data fine-tuning performance and how to approach creating better data for
+learning real-world capabilities over long contexts.
+
+
+
+
+ + ♻ ☆ Exploring Concept Depth: How Large Language Models Acquire Knowledge at + Different Layers? COLING 2025 +
+
+
+
+
+
+
+
+ Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
+
+
+ Large language models (LLMs) have shown remarkable performances across a wide
+range of tasks. However, the mechanisms by which these models encode tasks of
+varying complexities remain poorly understood. In this paper, we explore the
+hypothesis that LLMs process concepts of varying complexities in different
+layers, introducing the idea of ``Concept Depth'' to suggest that more complex
+concepts are typically acquired in deeper layers. Specifically, we categorize
+concepts based on their level of abstraction, defining them in the order of
+increasing complexity within factual, emotional, and inferential tasks. We
+conduct extensive probing experiments using layer-wise representations across
+various LLM families (Gemma, LLaMA, Qwen) on various datasets spanning the
+three domains of tasks. Our findings reveal that models could efficiently
+conduct probing for simpler tasks in shallow layers, and more complex tasks
+typically necessitate deeper layers for accurate understanding. Additionally,
+we examine how external factors, such as adding noise to the input and
+quantizing the model weights, might affect layer-wise representations. Our
+findings suggest that these factors can impede the development of a conceptual
+understanding of LLMs until deeper layers are explored. We hope that our
+proposed concept and experimental insights will enhance the understanding of
+the mechanisms underlying LLMs. Our codes are available at
+\url{https://github.com/Luckfort/CD}.
+
+
+
+ comment: COLING 2025
+
+
+
+ + ♻ ☆ Token-Budget-Aware LLM Reasoning +
+ +
+ Reasoning is critical for large language models (LLMs) to excel in a wide
+range of tasks. While methods like Chain-of-Thought (CoT) reasoning enhance LLM
+performance by decomposing problems into intermediate steps, they also incur
+significant overhead in token usage, leading to increased costs. We find that
+the reasoning process of current LLMs is unnecessarily lengthy and it can be
+compressed by including a reasonable token budget in the prompt, but the choice
+of token budget plays a crucial role in the actual compression effectiveness.
+We then propose a token-budget-aware LLM reasoning framework, which dynamically
+estimates token budgets for different problems based on reasoning complexity
+and uses the estimated token budgets to guide the reasoning process.
+Experiments show that our method effectively reduces token costs in CoT
+reasoning with only a slight performance reduction, offering a practical
+solution to balance efficiency and accuracy in LLM reasoning. Code:
+https://github.com/GeniusHTX/TALE.
+
+
+
+
+ + ♻ ☆ Large Language Models-guided Dynamic Adaptation for Temporal Knowledge + Graph Reasoning +
+
+
+
+
+
+
+
+ Jiapu Wang, Kai Sun, Linhao Luo, Wei Wei, Yongli Hu, Alan Wee-Chung Liew, Shirui Pan, Baocai Yin
+
+
+ Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing
+temporal information to capture complex relations within a Temporal Knowledge
+Graph (TKG) to infer new knowledge. Conventional methods in TKGR typically
+depend on deep learning algorithms or temporal logical rules. However, deep
+learning-based TKGRs often lack interpretability, whereas rule-based TKGRs
+struggle to effectively learn temporal rules that capture temporal patterns.
+Recently, Large Language Models (LLMs) have demonstrated extensive knowledge
+and remarkable proficiency in temporal reasoning. Consequently, the employment
+of LLMs for Temporal Knowledge Graph Reasoning (TKGR) has sparked increasing
+interest among researchers. Nonetheless, LLMs are known to function as black
+boxes, making it challenging to comprehend their reasoning process.
+Additionally, due to the resource-intensive nature of fine-tuning, promptly
+updating LLMs to integrate evolving knowledge within TKGs for reasoning is
+impractical. To address these challenges, in this paper, we propose a Large
+Language Models-guided Dynamic Adaptation (LLM-DA) method for reasoning on
+TKGs. Specifically, LLM-DA harnesses the capabilities of LLMs to analyze
+historical data and extract temporal logical rules. These rules unveil temporal
+patterns and facilitate interpretable reasoning. To account for the evolving
+nature of TKGs, a dynamic adaptation strategy is proposed to update the
+LLM-generated rules with the latest events. This ensures that the extracted
+rules always incorporate the most recent knowledge and better generalize to the
+predictions on future events. Experimental results show that without the need
+of fine-tuning, LLM-DA significantly improves the accuracy of reasoning over
+several common datasets, providing a robust framework for TKGR tasks.
+
+
+