SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses

Can LLMs consistently improve their previous outputs for better results? For this to be true, LLMs would need to be better at discriminating among previously-generated alternatives, than generating initial responses. We explore the validity of this hypothesis in practice. We first formulate a unified framework that allows us to compare the generative and discriminative capability of any model on any task. In our resulting experimental analysis of several open-source and industrial LLMs, we observe that model’s are not reliably better at discriminating among previously-generated alternatives than generating initial responses. This finding challenges the notion that LLMs may be able to enhance their performance only through their own judgment.

Authors

• Dongwei Jiang Johns Hopkins University
• Jingyu Zhang Academy for Engineering and Technology, Fudan University, Shanghai, China Innovation Platform for Academicians of Hainan Province, Haikou, Hainan, China
• Orion Weller Johns Hopkins University
• Nathaniel Weir Johns Hopkins University
• Benjamin Van Durme Johns Hopkins University
• Daniel Khashabi Johns Hopkins University

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