Self-Evaluation Guided Beam Search for Reasoning (2305.00633v3)

Abstract: Breaking down a problem into intermediate steps has demonstrated impressive performance in LLM reasoning. However, the growth of the reasoning chain introduces uncertainty and error accumulation, making it challenging to elicit accurate final results. To tackle this challenge of uncertainty in multi-step reasoning, we introduce a stepwise self-evaluation mechanism to guide and calibrate the reasoning process of LLMs. We propose a decoding algorithm integrating the self-evaluation guidance via stochastic beam search. The self-evaluation guidance serves as a better-calibrated automatic criterion, facilitating an efficient search in the reasoning space and resulting in superior prediction quality. Stochastic beam search balances exploitation and exploration of the search space with temperature-controlled randomness. Our approach surpasses the corresponding Codex-backboned baselines in few-shot accuracy by $6.34\%$, $9.56\%$, and $5.46\%$ on the GSM8K, AQuA, and StrategyQA benchmarks, respectively. Experiment results with Llama-2 on arithmetic reasoning demonstrate the efficiency of our method in outperforming the baseline methods with comparable computational budgets. Further analysis in multi-step reasoning finds our self-evaluation guidance pinpoints logic failures and leads to higher consistency and robustness. Our code is publicly available at https://guideddecoding.github.io/.

Self-Evaluation Guided Beam Search for Reasoning

The paper, "Self-Evaluation Guided Beam Search for Reasoning," introduces an approach aimed at improving the accuracy and reliability of multi-step reasoning in LLMs. As the complexity of the tasks increases, LLMs face error accumulation and uncertainty, especially when the reasoning process involves a long chain of steps. The authors propose a stepwise self-evaluation mechanism driving a stochastic beam search to refine the LLMs’ ability to produce more accurate final predictions.

Key Contributions

1. Self-Evaluation Mechanism: The paper introduces a novel stepwise self-evaluation scheme integrated into the reasoning process. This mechanism provides a calibrated criterion for the generation model's outputs, specifically evaluating the logic and validity of each step during the reasoning chain progression.
2. Stochastic Beam Search: This paper integrates stochastic beam search with self-evaluation to balance the exploitation and exploration of the search space. Using temperature-controlled randomness, the beam search can gain higher diversity without compromising prediction quality. This enables efficient navigation through potential reasoning paths, preventing error accumulation.
3. Strong Empirical Results: The proposed approach demonstrates superior performance compared to Codex-backboned baselines, achieving an increase of 6.34%, 9.56%, and 5.46% in few-shot accuracy on the GSM8K, AQuA, and StrategyQA benchmarks, respectively. Particularly notable is the method's ability to pinpoint logic failures and improve consistency, therefore producing more robust outputs.

Implications and Future Directions

The research brings forward promising implications for the design of automatic reasoning systems using LLMs. Primarily, addressing uncertainty and error propagation via this self-evaluation guided approach fosters higher accuracy in complex tasks requiring multi-step reasoning. This has potential applications in fields demanding precise logical deductions, such as automated theorem proving, complex query resolution in databases, and decision-making support systems in healthcare.

Additionally, the fusion of stochastic beam search with the self-evaluation mechanism opens up new avenues for integrating human-like reflection and feedback processes into LLMs. This points to potential future developments in AI, focusing on self-correcting and adaptive systems that can evaluate and refine their outputs autonomously.

Conclusion

Overall, the "Self-Evaluation Guided Beam Search for Reasoning" paper constitutes a significant advancement in leveraging LLMs for complex reasoning tasks. Through strategic calibration of reasoning chains, this approach successfully minimizes logical inconsistencies and enhances prediction accuracy, providing a framework that could guide subsequent research and development in related domains.