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Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
March 28, 2024, 4:48 a.m. | Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang, Weiming Lu
cs.CL updates on arXiv.org arxiv.org
Abstract: 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. …
abstract arxiv attention capacity contrast cs.ai cs.cl feedback however intrinsic investigation key language language model large language large language model llm perspectives prompting refine research strategy the key through type
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