当事后诸葛亮并不百发百中：测试大语言模型反思思维的局限
When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models
AI总结：
该研究在无外部反馈的严格设置下测试大语言模型的自反思能力，发现其效果因任务、初始回答准确率和问题难度而异，并给出了自反思的应用决策指南与开源代码。
摘要：
近期研究表明，自反思提示能显著提升大语言模型（LLMs）的推理能力。然而，使用外部反馈作为停止准则让人们对LLMs模拟人类式自我反思能力的真实程度产生了质疑。本文中，我们在更严格的评估设置下厘清这些能力，该设置不允许任何形式的外部反馈。我们在此设置下的结果呈现出分化：自反思在TruthfulQA上提升了性能，却在HotpotQA上对结果产生负面影响。我们开展了后续分析以厘清这些模式的影响因素，发现自反思的作用同时受模型初始回答准确率的可靠性和整体问题难度的影响：具体而言，当模型初始回答正确率更低、整体问题难度更高时，自反思的收益最大。我们还发现自反思会降低多数投票的倾向。基于研究结果，我们提出了何时采用自反思的决策指南。我们在https://github.com/yanhong-lbh/LLM-SelfReflection-Eval发布了复现实验的代码库。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
