SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses
<|reference_start|>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 models 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.<|reference_end|>
@article{jiang2024self-[in]correct:,
    title={SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated
  Responses},
    author={Dongwei Jiang, Jingyu Zhang, Orion Weller, Nathaniel Weir, Benjamin
  Van Durme, Daniel Khashabi},
    journal={arXiv preprint arXiv:2404.04298},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04298},
    primaryClass={cs.AI cs.CL cs.LG}
}