3.1 SELF-CORRECTION WITH ORACLE FEEDBACK

In this section, we attempt to employ existing self-correction methods, adopting their setting (using
labels to guide the self-correction process), to examine their effectiveness in enhancing the perfor-
mance of LLMs on reasoning tasks.

3.1.1 EXPERIMENTAL SETUP
Benchmarks. We focus on diverse datasets that have shown significant improvements through self-
correction in the existing literature, including
• GSM8K (Cobbe et al., 2021): GSM8K comprises a test set of 1,319 linguistically diverse grade
school math word problems, curated by human problem writers. There is a notable improvement
of approximately 7% as evidenced by Kim et al. (2023) post self-correction.
LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET