Summary of Conceptmath: a Bilingual Concept-wise Benchmark For Measuring Mathematical Reasoning Of Large Language Models, by Yanan Wu et al.
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
by Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, Zhiqi Bai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng
First submitted to arxiv on: 22 Feb 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 Medium Difficulty summary: This paper introduces ConceptMath, a fine-grained benchmark for evaluating Large Language Models’ (LLMs) concept-wise mathematical reasoning. Unlike traditional benchmarks that focus on average accuracy, ConceptMath organizes math problems under a hierarchy of math concepts to assess LLMs at different granularity levels. The authors evaluate a range of LLMs using ConceptMath and find that while they achieve high average accuracies on traditional benchmarks, their performance varies significantly across different math concepts, with some models even failing on basic ones. To address these weaknesses, the authors propose an efficient fine-tuning strategy to enhance the mathematical abilities of existing LLMs. This work aims to guide developers in understanding the strengths and limitations of their models and facilitate the growth of foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper introduces a new way to test how well computers can solve math problems. The current tests only look at whether the computer gets the answer right or wrong, but this new test looks at which specific math skills the computer uses. The researchers tested many different types of computer models and found that even though they do very well on average, some are much better than others at solving certain math problems. To help computers get better at math, the researchers came up with a way to make them practice and improve. |
Keywords
» Artificial intelligence » Fine tuning
