A Theoretical Understanding of Self-Correction through In-context Alignment
<|reference_start|>A Theoretical Understanding of Self-Correction through In-context Alignment: Going beyond mimicking limited human experiences, recent studies show initial evidence that, like humans, large language models (LLMs) are capable of improving their abilities purely by self-correction, i.e., correcting previous responses through self-examination, in certain circumstances. Nevertheless, little is known about how such capabilities arise. In this work, based on a simplified setup akin to an alignment task, we theoretically analyze self-correction from an in-context learning perspective, showing that when LLMs give relatively accurate self-examinations as rewards, they are capable of refining responses in an in-context way. Notably, going beyond previous theories on over-simplified linear transformers, our theoretical construction underpins the roles of several key designs of realistic transformers for self-correction: softmax attention, multi-head attention, and the MLP block. We validate these findings extensively on synthetic datasets. Inspired by these findings, we also illustrate novel applications of self-correction, such as defending against LLM jailbreaks, where a simple self-correction step does make a large difference. We believe that these findings will inspire further research on understanding, exploiting, and enhancing self-correction for building better foundation models.<|reference_end|>
@article{wang2024a,
    title={A Theoretical Understanding of Self-Correction through In-context
  Alignment},
    author={Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang},
    journal={arXiv preprint arXiv:2405.18634},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18634},
    primaryClass={cs.LG cs.CL stat.ML}
}