Large Language Models (LLMs) have emerged as a groundbreaking technology
with their unparalleled text generation capabilities across various applications.
Nevertheless, concerns persist regarding the accuracy and appropriateness of their
generated content. A contemporary methodology, self-correction, has been pro-
posed as a remedy to these issues. Building upon this premise, this paper critically
examines the role and efficacy of self-correction within LLMs, shedding light on
its true potential and limitations. Central to our investigation is the notion of in-
trinsic self-correction, whereby an LLM attempts to correct its initial responses
based solely on its inherent capabilities, without the crutch of external feedback.
In the context of reasoning, our research indicates that LLMs struggle to self-
correct their responses without external feedback, and at times, their performance
might even degrade post self-correction. Drawing from these insights, we offer
LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET