Summary of Comat: Chain Of Mathematically Annotated Thought Improves Mathematical Reasoning, by Joshua Ong Jun Leang et al.
CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning
by Joshua Ong Jun Leang, Aryo Pradipta Gema, Shay B. Cohen
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Symbolic Computation (cs.SC)
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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 A novel approach to enhancing the mathematical reasoning capabilities of large language models (LLMs) is presented in this paper. The Chain of Mathematically Annotated Thought (CoMAT) framework improves upon previous prompting techniques like Chain-of-Thought (CoT) by converting natural language queries into symbolic form and then executing reasoning processes from these representations. CoMAT achieves state-of-the-art results across six out of seven benchmarks, with notable gains on the MMLU-Redux (MATH) and GaoKao MCQ datasets. The framework’s transparent reasoning process also ensures faithfulness and verifiability for complex mathematical tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have trouble with math problems, even with special prompts like Chain-of-Thought. This paper introduces a new way to help them called Chain of Mathematically Annotated Thought (CoMAT). It works by turning words into symbols and then using those symbols to figure out the answer. CoMAT does this all on its own, without needing any extra tools. In tests with four different language models, CoMAT did better than regular prompts on most math problems. Not only did it do better, but it also explained how it got the answers, making it more reliable and trustworthy. |
Keywords
* Artificial intelligence * Prompting
