Summary of Champ: a Competition-level Dataset For Fine-grained Analyses Of Llms’ Mathematical Reasoning Capabilities, by Yujun Mao et al.
CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs’ Mathematical Reasoning Capabilities
by Yujun Mao, Yoon Kim, Yilun Zhou
First submitted to arxiv on: 13 Jan 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 Medium Difficulty summary: Recent advancements in large language models (LLMs) have demonstrated impressive mathematical reasoning abilities on challenging problems, particularly when employing self-generated verbalizations of intermediate reasoning steps. However, current evaluations primarily focus on the final answer correctness, leaving unclear whether LLMs can effectively utilize helpful side information such as problem-specific hints. To address this gap, we propose a benchmark dataset, Concept and Hint-Annotated Math Problems (CHAMP), consisting of high school math competition problems annotated with concepts, hints, and related problems. This corpus allows us to explore the effects of additional information, including relevant hints, misleading concepts, or related problems. Our findings indicate that some models can benefit from these annotations, while others struggle to verify their solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers have been studying how computers can solve math problems like humans do. They’ve found that some computer programs are really good at solving tricky math problems if they’re given hints or extra information. The problem is that we don’t know which hints or information will help the most. To answer this question, scientists created a special set of math problems with hints and extra details. They tested how well different computer programs could solve these problems and found that some programs got better when they had more information, while others got worse. This helps us understand how computers can get smarter at solving math problems. |
