CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908767955714048 |
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| author | Leang, Joshua Ong Jun Gema, Aryo Pradipta Cohen, Shay B. |
| author_facet | Leang, Joshua Ong Jun Gema, Aryo Pradipta Cohen, Shay B. |
| contents | Mathematical reasoning remains a significant challenge for large language models (LLMs), despite progress in prompting techniques such as Chain-of-Thought (CoT). We present **Chain of Mathematically Annotated Thought (CoMAT)**, which enhances reasoning through two stages: *Symbolic Conversion* (converting natural language queries into symbolic form) and *Reasoning Execution* (deriving answers from symbolic representations). CoMAT operates entirely with a single LLM and without external solvers. Across four LLMs, CoMAT outperforms traditional CoT on six out of seven benchmarks, achieving gains of 4.48% on MMLU-Redux (MATH) and 4.58% on GaoKao MCQ. In addition to improved performance, CoMAT ensures faithfulness and verifiability, offering a transparent reasoning process for complex mathematical tasks |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning Leang, Joshua Ong Jun Gema, Aryo Pradipta Cohen, Shay B. Artificial Intelligence Computation and Language Machine Learning Symbolic Computation Mathematical reasoning remains a significant challenge for large language models (LLMs), despite progress in prompting techniques such as Chain-of-Thought (CoT). We present **Chain of Mathematically Annotated Thought (CoMAT)**, which enhances reasoning through two stages: *Symbolic Conversion* (converting natural language queries into symbolic form) and *Reasoning Execution* (deriving answers from symbolic representations). CoMAT operates entirely with a single LLM and without external solvers. Across four LLMs, CoMAT outperforms traditional CoT on six out of seven benchmarks, achieving gains of 4.48% on MMLU-Redux (MATH) and 4.58% on GaoKao MCQ. In addition to improved performance, CoMAT ensures faithfulness and verifiability, offering a transparent reasoning process for complex mathematical tasks |
| title | CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning |
| topic | Artificial Intelligence Computation and Language Machine Learning Symbolic Computation |
| url | https://arxiv.org/abs/2410.10336 |