Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.17349 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916335206793216 |
|---|---|
| author | Ding, Yuyang Hu, Hanglei Zhou, Jie Chen, Qin Jiang, Bo He, Liang |
| author_facet | Ding, Yuyang Hu, Hanglei Zhou, Jie Chen, Qin Jiang, Bo He, Liang |
| contents | With the introduction of large language models (LLMs), automatic math reasoning has seen tremendous success. However, current methods primarily focus on providing solutions or using techniques like Chain-of-Thought to enhance problem-solving accuracy. In this paper, we focus on improving the capability of mathematics teaching via a Socratic teaching-based LLM (\texttt{SocraticLLM}), which guides learners toward profound thinking with clarity and self-discovery via conversation. We collect and release a high-quality mathematical teaching dataset, named \texttt{SocraticMATH}, which provides Socratic-style conversations of problems with extra knowledge. Also, we propose a knowledge-enhanced LLM as a strong baseline to generate reliable responses with review, guidance/heuristic, rectification, and summarization. Experimental results show the great advantages of \texttt{SocraticLLM} by comparing it with several strong generative models. The codes and datasets are available on \url{https://github.com/ECNU-ICALK/SocraticMath}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Boosting Large Language Models with Socratic Method for Conversational Mathematics Teaching Ding, Yuyang Hu, Hanglei Zhou, Jie Chen, Qin Jiang, Bo He, Liang Computation and Language With the introduction of large language models (LLMs), automatic math reasoning has seen tremendous success. However, current methods primarily focus on providing solutions or using techniques like Chain-of-Thought to enhance problem-solving accuracy. In this paper, we focus on improving the capability of mathematics teaching via a Socratic teaching-based LLM (\texttt{SocraticLLM}), which guides learners toward profound thinking with clarity and self-discovery via conversation. We collect and release a high-quality mathematical teaching dataset, named \texttt{SocraticMATH}, which provides Socratic-style conversations of problems with extra knowledge. Also, we propose a knowledge-enhanced LLM as a strong baseline to generate reliable responses with review, guidance/heuristic, rectification, and summarization. Experimental results show the great advantages of \texttt{SocraticLLM} by comparing it with several strong generative models. The codes and datasets are available on \url{https://github.com/ECNU-ICALK/SocraticMath}. |
| title | Boosting Large Language Models with Socratic Method for Conversational Mathematics Teaching |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.17349 |