Summary of Utmath: Math Evaluation with Unit Test Via Reasoning-to-coding Thoughts, by Bo Yang et al.
UTMath: Math Evaluation with Unit Test via Reasoning-to-Coding Thoughts
by Bo Yang, Qingping Yang, Yingwei Ma, Runtao Liu
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces a new evaluation framework, called UTMath Benchmark, designed to assess Large Language Models’ (LLMs) mathematical reasoning capabilities. It addresses limitations in existing benchmarks like GSM8K and MATH by introducing 1,053 cutting-edge problems across nine mathematical domains. The best-performing model solves only about one-third of the problems, highlighting the challenges in mathematical reasoning. To facilitate more sophisticated solutions, the paper presents the Reasoning-to-Coding of Thoughts (RCoT) approach, which encourages LLMs to engage in explicit reasoning before code generation. Additionally, it releases a training dataset and provides access to the benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The UTMath Benchmark is a new evaluation framework for Large Language Models that helps measure their mathematical reasoning capabilities. The paper introduces 1,053 problems across nine math domains to test models’ accuracy and generality. Only the best-performing model can solve about one-third of the problems, showing how challenging it is to reason mathematically. To help models do better, the paper suggests a new approach called Reasoning-to-Coding of Thoughts. |
