# Using Language Models For Knowledge Acquisition in Natural Language Reasoning Problems

**Authors:** Fangzhen Lin, Ziyi Shou, Chengcai Chen  
**arXiv:** [2304.01771](https://arxiv.org/abs/2304.01771)  
**Date:** 2023-04-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2304.01771v1/

## Key points

- The paper explores the use of large language models (LLMs) to solve natural language problems that require non-trivial reasoning.
- Two methods of using LLMs are compared: asking them to directly solve the problem or extracting facts from the problem text and using a theorem prover to solve it.
- ChatGPT and GPT4 were used on a series of logic word puzzles, and the authors conclude that the latter approach is more effective.
- The paper highlights the importance of knowledge acquisition in natural language processing tasks.
- Combining LLMs with other tools can improve performance.
- This research contributes to ongoing efforts to develop more sophisticated natural language processing techniques for complex problem-solving tasks.

## Summary

In their paper titled "Using Language Models For Knowledge Acquisition in Natural Language Reasoning Problems," authors Fangzhen Lin, Ziyi Shou, and Chengcai Chen explore the use of large language models (LLMs) to solve natural language problems that require non-trivial reasoning. They compare two methods of using LLMs: asking them to directly solve the problem or extracting facts from the problem text and using a theorem prover to solve it. The authors use ChatGPT and GPT4 on a series of logic word puzzles and conclude that the latter approach is more effective. The paper highlights the importance of knowledge acquisition in natural language processing tasks and suggests that combining LLMs with other tools can improve performance. This research contributes to ongoing efforts to develop more sophisticated natural language processing techniques for complex problem-solving tasks.