When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models

• URL: http://arxiv.org/abs/2404.09129v1
• Date: Sun, 14 Apr 2024 02:47:32 GMT
• Title: When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models
• Authors: Yanhong Li, Chenghao Yang, Allyson Ettinger,
• Abstract summary: Self-reflection enhances performance in TruthfulQA, but adversely affects results in HotpotQA. We find that self-reflection shows the most benefit when models are less likely to be correct initially, and when overall question difficulty is higher. Based on our findings, we propose guidelines for decisions on when to implement self-reflection.
• Score: 15.781930031346105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies suggest that self-reflective prompting can significantly enhance the reasoning capabilities of Large Language Models (LLMs). However, the use of external feedback as a stop criterion raises doubts about the true extent of LLMs' ability to emulate human-like self-reflection. In this paper, we set out to clarify these capabilities under a more stringent evaluation setting in which we disallow any kind of external feedback. Our findings under this setting show a split: while self-reflection enhances performance in TruthfulQA, it adversely affects results in HotpotQA. We conduct follow-up analyses to clarify the contributing factors in these patterns, and find that the influence of self-reflection is impacted both by reliability of accuracy in models' initial responses, and by overall question difficulty: specifically, self-reflection shows the most benefit when models are less likely to be correct initially, and when overall question difficulty is higher. We also find that self-reflection reduces tendency toward majority voting. Based on our findings, we propose guidelines for decisions on when to implement self-reflection. We release the codebase for reproducing our experiments at https://github.com/yanhong-lbh/LLM-SelfReflection-Eval.

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