# Boosting Language Models Reasoning with Chain-of-Knowledge Prompting

**Authors:** Jianing Wang, Qiushi Sun, Nuo Chen, Xiang Li, Ming Gao  
**arXiv:** [2306.06427](https://arxiv.org/abs/2306.06427)  
**Date:** 2023-06-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2306.06427v2/

## Key points

- Introduction of Chain-of-Knowledge (CoK) prompting method to enhance reasoning capabilities of Large Language Models (LLMs)
- CoK decomposes reasoning chains into explicit pieces of knowledge evidence in the form of structured triples
- Proposal of F^2-Verification to estimate reliability and prompt rethinking, addressing brittleness and inaccuracies
- Significant improvement observed in various reasoning tasks through experiments
- Future plans include enhancing larger LLMs, integrating real-time verification using search engines, and conducting interpretability analysis on LLMs' reasoning processes

## Summary

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In this study, we introduce a novel prompting method called Chain-of-Knowledge (CoK) to enhance the reasoning capabilities of Large Language Models (LLMs). CoK aims to decompose reasoning chains into explicit pieces of knowledge evidence in the form of structured triples, inspired by human behavior. To address brittleness and inaccuracies, we propose F^2-Verification to estimate reliability and prompt rethinking. Our experiments show significant improvement in various reasoning tasks. Moving forward, we plan to enhance larger LLMs, integrate real-time verification using search engines, and conduct interpretability analysis on LLMs' reasoning processes. Overall, our study showcases the effectiveness of CoK prompting for complex tasks.