Summary of Learning Mathematical Rules with Large Language Models, by Antoine Gorceix et al.
Learning Mathematical Rules with Large Language Models
by Antoine Gorceix, Bastien Le Chenadec, Ahmad Rammal, Nelson Vadori, Manuela Veloso
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 investigates the capacity of large language models to acquire specific mathematical rules, including distributivity and simplifying equations. Researchers developed a rigorous methodology for creating synthetic data containing these rules and fine-tuned large language models on this data. The results show that the model can learn and generalize these rules to some extent, as well as apply them correctly in word problem contexts. This study contributes to our understanding of large language models’ mathematical abilities and potential applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can learn specific math rules like distributivity or simplifying equations. The researchers made a special way to create fake data with these rules and then trained the model on this data. They found that the model can learn some of these rules and use them correctly in word problems. This study helps us understand what large language models can do and how they might be used. |
Keywords
» Artificial intelligence » Synthetic data
