Authors:
(1) Zhan Ling, UC San Diego and equal contribution;
(2) Yunhao Fang, UC San Diego and equal contribution;
(3) Xuanlin Li, UC San Diego;
(4) Zhiao Huang, UC San Diego;
(5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research
(6) Roland Memisevic, Qualcomm AI Research;
(7) Hao Su, UC San Diego.
Table of Links
Motivation and Problem Formulation
Deductively Verifiable Chain-of-Thought Reasoning
Conclusion, Acknowledgements and References
A Deductive Verification with Vicuna Models
C More Details on Answer Extraction
E More Deductive Verification Examples
7 Conclusion
In this paper, we aim to enable Large Language Models (LLMs) to perform explicit and rigorous deductive reasoning while ensuring the trustworthiness of their reasoning processes through self-verification. To this end, we have proposed a novel framework based on “Natural Program”, a natural language-based deductive reasoning format that facilitates reasoning verification and can be easily generated through in-context learning. Within this framework, we decompose the verification process of complex reasoning chains into step-by-step subprocesses that focus solely on necessary context and premises, allowing us to significantly enhance the accuracy of verification. Additionally, we introduce a Unanimity-Plurality Voting strategy to further improve verification accuracy. Experimentally, we demonstrate the superiority of our framework in improving the rigor, trustworthiness, and interpretability of reasoning steps and answers.
Broader Impact. While our deductive verification approach can mitigate hallucinations and reasoning errors of Large Language Models (LLMs), it does not completely eliminate these phenomena. LLMs can still produce harmful and biased content, make incorrect claims, and produce wrongful advice. This issue becomes particularly significant when LLMs engage in complex reasoning chains, increasing the risk of misleading users. Consequently, it is still crucial for users to exercise great caution when interacting with, deploying, or developing LLM-based applications.
Acknowledgements
We would like to express our sincere gratitude to Tongzhou Mu and Caiwei Xiao from UC San Diego, Kairong Luo from Tsinghua University, and Pulkit Madan, Reza Pourreza, Sunny Panchal, and Apratim Bhattacharyya from Qualcomm for their valuable discussions and feedback.
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