Large Language Models Cannot Self-Correct Reasoning Yet
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
proposed 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 intrinsic 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 suggestions
for future research and practical applications in this field.
651cdd0628950a077034c66a
Jie Huang
claimed_verified
2023-10-16T07:25:03.502Z
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Jie Huang
user
jeffhj
651cdd0628950a077034c66b
Xinyun Chen
651cdd0628950a077034c66c
Swaroop Mishra
admin_assigned
2023-10-04T12:39:09.554Z
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/avatars/7a79362a24b844860781fb0625527eae.svg
Swaroop Mishra
user
swaroop7
651cdd0628950a077034c66d
Huaixiu Steven Zheng
651cdd0628950a077034c66e
Adams Wei Yu
651cdd0628950a077034c66f
Xinying Song
651cdd0628950a077034c670
Denny Zhou
extracted_confirmed
2024-02-20T16:43:56.732Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
Denny Zhou
user
dennyzhou
Large Language Models Cannot Self-Correct Reasoning Yet
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
proposed 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 intrinsic 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 suggestions
for future research and practical applications in this field.
This paper evaluates the intrinsic self-correction capabilities of Large Language Models and identifies limitations in their ability to improve responses without external feedback.
Large Language Models LLMs self-correction intrinsic self-correction