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Deductive Verification of Chain-of-Thought Reasoning (2306.03872v3)

Published 6 Jun 2023 in cs.CL, cs.AI, and cs.LG
Deductive Verification of Chain-of-Thought Reasoning

Abstract: 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 LLMs 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 LLMs 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 https://github.com/lz1oceani/verify_cot.

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Deductive Verification of Chain-of-Thought Reasoning

The paper "Deductive Verification of Chain-of-Thought Reasoning" explores enhancing LLMs through a rigorous verification approach that mitigates common issues associated with Chain-of-Thought (CoT) prompting. While CoT prompting aids in producing comprehensive reasoning, it is susceptible to hallucinations and errors, necessitating a reliable verification mechanism.

Overview

The authors address a significant limitation of LLMs, which, despite their capabilities, often stumble on cogent reasoning due to accumulated errors in intermediate steps. Inspired by human deductive reasoning processes, this paper introduces a structured approach to break down reasoning verification into manageable subprocesses. This is achieved through the introduction of the "Natural Program," a format designed to enable precise and valid reasoning steps.

Methodology

The Natural Program format serves as the cornerstone of this approach. It ensures that each reasoning step is explicitly supported by necessary premises, curtailing instances of extraneous information that may hinder logical deductions. By leveraging this structured format, models are trained to perform reasoning verification iteratively, effectively identifying and addressing errors at each step before proceeding.

Significant emphasis is placed on decomposing the verification process. Rather than attempting to validate an entire reasoning chain at once, the paper advocates for a step-by-step confirmation, promoting accuracy and reducing the likelihood of oversight. The Natural Program format ensures that LLMs can self-verify, enhancing both the rigor and trustworthiness of the reasoning process.

Experimental Results

Experiments conducted across various datasets, particularly in arithmetic and commonsense reasoning, demonstrate the framework's efficacy. The application of deductive verification markedly improved the correctness of solutions on complex reasoning tasks, as evidenced by numerical evaluations on benchmarks such as GSM8K and MATH. Notably, the rigorous format allowed for coherent and traceable reasoning paths, improving overall performance.

Implications and Future Work

The implications of this research in AI are substantial. By instilling a rigorous verification method, LLMs can potentially be adapted to domains that demand high accuracy and reliability, such as legal reasoning or scientific research. Additionally, the reduction of hallucinations—a persistent issue in LLM deployment—enhances user trust and model applicability.

Future developments may focus on further refining the verification process, perhaps extending the Natural Program format to accommodate even more complex reasoning structures or integrating additional modules that allow context adaptation without retraining. Another avenue could involve exploring alternative means of detecting and addressing context irrelevancies in reasoning, thereby pushing the boundaries of what LLMs can achieve in terms of precise and reliable outputs.

In conclusion, the paper's contribution is a significant advancement towards creating more reliable and trustworthy AI systems through meticulous deductive verification of CoT reasoning, setting a foundational paradigm for future enhancements in LLM reasoning capabilities.

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Authors (7)
  1. Zhan Ling (16 papers)
  2. Yunhao Fang (11 papers)
  3. Xuanlin Li (18 papers)
  4. Zhiao Huang (28 papers)
  5. Mingu Lee (16 papers)
  6. Roland Memisevic (36 papers)
  7. Hao Su (217 papers)
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