Summary of Hardmath: a Benchmark Dataset For Challenging Problems in Applied Mathematics, by Jingxuan Fan et al.
HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics
by Jingxuan Fan, Sarah Martinson, Erik Y. Wang, Kaylie Hausknecht, Jonah Brenner, Danxian Liu, Nianli Peng, Corey Wang, Michael P. Brenner
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 HARDMath, a new dataset featuring challenging applied mathematics problems that require analytical approximation techniques. The dataset is inspired by a graduate course on asymptotic methods and aims to address the underrepresentation of advanced applied mathematics problems in existing Large Language Model (LLM) benchmark datasets. The framework auto-generates a large number of problems with solutions validated against numerical ground truths. The paper evaluates both open- and closed-source LLMs on HARDMath-mini, a sub-sampled test set of 366 problems, as well as on 40 word problems formulated in applied science contexts. The results demonstrate the limitations of current LLM performance on advanced graduate-level applied math problems and underscore the importance of datasets like HARDMath to advance mathematical abilities of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset called HARDMath that has hard math problems for language models to solve. These problems are like what you would see in a college or university math class. The researchers made this dataset so they can test how good these language models are at doing math. They used different kinds of math problems and tested the best language models on them. Even the best ones did pretty poorly, which means we need better datasets to help language models get better at math. |
Keywords
» Artificial intelligence » Large language model
