Summary of Regress, Don’t Guess — a Regression-like Loss on Number Tokens For Language Models, by Jonas Zausinger et al.
Regress, Don’t Guess – A Regression-like Loss on Number Tokens for Language Models
by Jonas Zausinger, Lars Pennig, Kacper Chlodny, Vincent Limbach, Anna Ketteler, Thorben Prein, Vishwa Mohan Singh, Michael Morris Danziger, Jannis Born
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitation of language models in tasks involving reasoning over quantities, particularly arithmetic operations. Current models struggle to generate numbers and lack an inductive bias for doing so, which is crucial in scientific datasets where both text and numerical data coexist. The authors propose two novel loss functions that can be added to any language model: an Lp loss and a Wasserstein-1 distance-based loss. These regression-like losses aim to improve the model’s ability to generate numbers accurately. The proposed methods are tested on a mathematics dataset, comparing favorably with existing tokenization, encoding, and decoding schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with language models that struggle to work with numbers. Right now, they’re great at generating text but terrible at math problems like addition or multiplication. This is because their training data assumes words are the only things that matter. To fix this, the authors create two new ways for language models to learn about numbers. These methods can be used in any language model and help it generate more accurate numbers. The authors test these methods on a dataset of math problems and show that they work better than other approaches. |
Keywords
» Artificial intelligence » Language model » Regression » Tokenization
