post-hoc prompting, wherein the prompting involves the responses of LLMs. We assess scenarios
where self-correction is genuinely beneficial—specifically when it provides instructions or feedback
that standard prompting misses. Moreover, our analysis reveals that the enhancement attributed to
self-correction in certain tasks may stem from an ill-crafted initial instruction that is overshadowed
by a carefully-crafted feedback prompt. In such cases, integrating the feedback into the initial in-
struction or crafting a better initial prompt might yield better results and lower the inference cost.
In light of our findings, we provide insights into the nuances of LLMs’ self-correction capabilities,
urging the community to be skeptical of this idea and apply it with caution. Furthermore, we ini-
tiate discussions and encourage future research to delve into methods that can genuinely correct
reasoning, for instance, by incorporating external feedback.
LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET