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 Introducing "Number Token Loss" (NTL) for language models to improve numerical reasoning by using regression-based loss functions that account for the proximity of numbers, achieving better performance on math tasks without increasing computational overhead.
 
-## Resources
-Find our paper [here](https://arxiv.org/abs/2411.02083) and the poster of the NeurIPS 2024 MathAI workshop [here](https://github.com/tum-ai/number-token-loss/blob/main/resources/neurips_mathai_poster.pdf "Poster")
-
 ## Setup
 
 ### Via Python
@@ -201,13 +198,3 @@ e.g for Standard T5 + **NTL-WAS**:
 python src/ntl/run_language_modeling.py model_args=vanilla_t5_ntl  model_args.number_token_loss_with_wasserstein=true training_args=eval model_args.model_name_or_path=<path to checkpoint file>
 ```
 
-
-## Citation
-If you use this work, please cite:
-```bib
-@inproceedings{zausinger24regress,
-  title={Regress, Don't Guess--A Regression-like Loss on Number Tokens for Language Models},
-  author={Zausinger, Jonas and Pennig, Lars and Chlodny, Kacper and Limbach, Vincent and Ketteler, Anna and Prein, Thorben and Singh, Vishwa Mohan and Danziger, Michael and Born, Jannis},
-  booktitle={The 4th Workshop on Mathematical Reasoning and AI at NeurIPS'24},
-  year={2024}
-}
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