Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
CoRR(2024)
摘要
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.
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