Summary of Evaluating Mathematical Reasoning Of Large Language Models: a Focus on Error Identification and Correction, by Xiaoyuan Li et al.
Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction
by Xiaoyuan Li, Wenjie Wang, Moxin Li, Junrong Guo, Yang Zhang, Fuli Feng
First submitted to arxiv on: 2 Jun 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 The paper presents a comprehensive evaluation of Large Language Models (LLMs) in mathematical reasoning, focusing on the examiner’s perspective and error identification/correction tasks. The authors design four evaluation tasks and create a new dataset with annotated error types and steps to assess 11 representative LLMs, including GPT-4, LLaMA-2-7B, GPT-3.5, and Gemini Pro. The results show that GPT-4 outperforms all models, while LLaMA-2-7B demonstrates comparable abilities to closed-source models. Notably, calculation error is the most challenging type, and prompting LLMs with error types can improve correction accuracy by 47.9%. This work reveals potential directions for developing mathematical reasoning abilities in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well big language models do math problems. Right now, people are mostly checking if the model gets the right answer, but this paper also looks at how good the model is at finding and fixing its own mistakes. The authors created special tasks to test 11 different models, including some new ones that can do math really well. They found out which models were best at math, and it’s not just about getting the right answer – it’s also about being able to explain how you got there. |
Keywords
» Artificial intelligence » Gemini » Gpt » Llama » Prompting
