using oracles to guide the self-correction process, and the improvements vanish when oracle labels
are not available. Transitioning to another facet of self-correction, we investigate the potential of
multi-agent debate (Du et al., 2023; Liang et al., 2023) as a means to improve reasoning. In this
method, multiple instances of an LLM critique each other’s responses. However, our results reveal
that its efficacy is no better than self-consistency (Wang et al., 2022) when considering an equivalent
number of responses, highlighting the limitations of such an approach.
On the other hand, while self-correction has limitations in enhancing reasoning, it does show
promising results in other tasks like altering the style or improving the appropriateness of responses
(Madaan et al., 2023; Bai et al., 2022; Ganguli et al., 2023). Thus, understanding the nuances and
identifying the underlying reasons is vital. To delve deeper, we perceive self-correction as a form of
LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET