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Donate arxiv logo > cs > arXiv:2306.03872 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2306.03872 (cs) [Submitted on 6 Jun 2023 (v1), last revised 3 Oct 2023 (this version, v3)] Title:Deductive Verification of Chain-of-Thought Reasoning Authors:Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, Hao Su View a PDF of the paper titled Deductive Verification of Chain-of-Thought Reasoning, by Zhan Ling and 5 other authors View PDF Abstract:Large Language Models (LLMs) significantly benefit from Chain-of-Thought (CoT) prompting in performing various reasoning tasks. While CoT allows models to produce more comprehensive reasoning processes, its emphasis on intermediate reasoning steps can inadvertently introduce hallucinations and accumulated errors, thereby limiting models' ability to solve complex reasoning tasks. Inspired by how humans engage in careful and meticulous deductive logical reasoning processes to solve tasks, we seek to enable language models to perform explicit and rigorous deductive reasoning, and also ensure the trustworthiness of their reasoning process through self-verification. However, directly verifying the validity of an entire deductive reasoning process is challenging, even with advanced models like ChatGPT. In light of this, we propose to decompose a reasoning verification process into a series of step-by-step subprocesses, each only receiving their necessary context and premises. To facilitate this procedure, we propose Natural Program, a natural language-based deductive reasoning format. Our approach enables models to generate precise reasoning steps where subsequent steps are more rigorously grounded on prior steps. It also empowers language models to carry out reasoning self-verification in a step-by-step manner. By integrating this verification process into each deductive reasoning stage, we significantly enhance the rigor and trustfulness of generated reasoning steps. Along this process, we also improve the answer correctness on complex reasoning tasks. Code will be released at this https URL. Comments: Published at NeurIPS 2023 Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2306.03872 [cs.CL] (or arXiv:2306.03872v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2306.03872 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhan Ling [view email] [v1] Tue, 6 Jun 2023 17:18:56 UTC (313 KB) [v2] Wed, 7 Jun 2023 00:37:34 UTC (402 KB) [v3] Tue, 3 Oct 2023 19:48:22 UTC (159 KB) Full-text links: Access Paper: View a PDF of the paper titled Deductive Verification of Chain-of-Thought Reasoning, by Zhan Ling and 5 other authors * View PDF * TeX Source * Other Formats license icon view license Current browse context: cs.CL < prev | next > new | recent | 2023-06 Change to browse by: cs cs.AI cs.LG References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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