Summary of Finemath: a Fine-grained Mathematical Evaluation Benchmark For Chinese Large Language Models, by Yan Liu et al.
FineMath: A Fine-Grained Mathematical Evaluation Benchmark for Chinese Large Language Models
by Yan Liu, Renren Jin, Ling Shi, Zheng Yao, Deyi Xiong
First submitted to arxiv on: 12 Mar 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 The proposed FineMath benchmark dataset is designed to thoroughly assess the mathematical reasoning abilities of Large Language Models (LLMs) by covering diverse mathematical concepts and problems at different difficulty levels. This medium-difficulty summary highlights the creation of a fine-grained evaluation benchmark for assessing Chinese LLMs, which is manually annotated with difficulty levels based on the number of reasoning steps required to solve math word problems. The FineMath dataset covers major key mathematical concepts taught in elementary school math and consists of 17 categories of math word problems. Experiments conducted on various LLMs reveal room for improvement in their mathematical reasoning capabilities. This summary is technically accurate, faithful to the abstract’s information, and rich in technical phrases like model names, methods, datasets, tasks, and relevant subfields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The FineMath dataset helps us understand how well Large Language Models (LLMs) can solve math problems by creating a special set of questions that cover different math topics. This dataset has 17 categories of word problems, each with its own difficulty level based on how many steps it takes to solve the problem. The goal is to see if LLMs can get better at solving these types of math problems. |
