Summary of Mathchat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-turn Interactions, by Zhenwen Liang et al.
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions
by Zhenwen Liang, Dian Yu, Wenhao Yu, Wenlin Yao, Zhihan Zhang, Xiangliang Zhang, Dong Yu
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper investigates the capabilities of large language models (LLMs) in mathematical problem-solving, specifically focusing on multi-turn question answering formats. The authors introduce MathChat, a comprehensive benchmark designed to evaluate LLMs’ performance across various mathematical tasks. They assess the abilities of state-of-the-art (SOTA) LLMs on this benchmark and find that while they excel in single-turn question answering, they significantly underperform in more complex scenarios requiring sustained reasoning and dialogue understanding. To address these limitations, the authors develop MathChat sync, a synthetic dialogue-based math dataset for fine-tuning LLMs to improve their interaction and instruction-following capabilities in conversations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models (LLMs) can solve math problems that involve back-and-forth conversation. Right now, these models are great at answering simple math questions one at a time. But they don’t do as well when they need to have a longer conversation or generate an open-ended answer. The authors of this paper created a special test called MathChat to see how well different LLMs can handle these kinds of math problems. They found that the best models are still not very good at having conversations about math. To help them get better, they made a new dataset called MathChat sync that trains the models to follow instructions and have more natural conversations. |
Keywords
» Artificial intelligence » Fine tuning » Question answering
