The reflection capacity of Large Language Model (LLM) has garnered extensive
attention. A post-hoc prompting strategy, e.g., reflexion and self-refine,
refines LLM's response based on self-evaluated or external feedback. However,
recent research indicates without external feedback, LLM's intrinsic reflection
is unstable. Our investigation unveils that the key bottleneck is the quality
of the self-evaluated feedback. We find LLMs often exhibit overconfidence or
high randomness when self-evaluate, offering stubborn or inconsistent feedback,
which causes poor reflection. To remedy this, we advocate Self-Contrast: It
adaptively explores diverse solving perspectives tailored to the request,
contrasts the differences, and summarizes these discrepancies into a checklist
which could be used to re-examine and eliminate discrepancies. Our method
endows LLM with diverse perspectives to alleviate stubborn biases. Moreover,
their discrepancies indicate potential errors or inherent uncertainties that
LLM often overlooks. Reflecting upon these can catalyze more accurate and
stable reflection. Experiments conducted on a series of reasoning and
translation tasks with different LLMs serve to underscore the effectiveness and
generality of our strategy.

我们提出了一种基于自对照的策略，通过适应性地探索多样的解决观点、对比差异并总结出这些差异的清单，为大型语言模型增添了多样的视角，以减轻固执偏见，并且更准确和稳定地进行反思。