In the preceding section, we observed
that LLMs
in self-
correcting their reasoning. Yet, there are
cases where self-correction has yielded
impressive results, as evidenced by stud-
ies presented in Madaan et al. (2023);
Bai et al. (2022); Ganguli et al. (2023).
Therefore,
to discern the
differences and pinpoint the underlying
causes.
To unravel this, it is important to grasp
the fundamental nature of self-correction. Based on its form, self-correction can be viewed as a type
of post-hoc prompting. The difference between it and standard prompting (which we term pre-hoc
prompting here) is that the prompting is conducted on top of the responses of LLMs. And we refer
to the process of improving such prompts as post-hoc prompt engineering.
Therefore, scenarios in which self-correction enhances model responses occur when it can provide
valuable instruction or feedback that pre-hoc prompting cannot. For instance, when the goal is to
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