LLMs refine their responses based on feedback to their previous outputs (Madaan et al., 2023;
Welleck et al., 2023; Shinn et al., 2023; Kim et al., 2023; Bai et al., 2022; Ganguli et al., 2023;
Gao et al., 2023; Paul et al., 2023; Chen et al., 2023b; Pan et al., 2023, inter alia). However, the
underlying mechanics and efficacy of self-correction in LLMs remain underexplored. A fundamen-
tal question arises: If an LLM possesses the ability to self-correct, why doesn’t it simply offer the
correct answer in its initial attempt? This paper delves deeply into this paradox, critically examining
the self-correction capabilities of LLMs, with a particular emphasis on reasoning (Wei et al., 2022;
Zhou et al., 2023b; Huang & Chang, 2023).
To study this, we first define the concept of intrinsic self-correction, a scenario wherein the model
endeavors to rectify its initial responses based solely on its inherent capabilities, without the crutch
LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET