  While self-correction has shown promise in improving LLM outputs in terms of
style and quality (e.g. Chen et al., 2023b; Madaan et al., 2023), recent
attempts to self-correct logical or reasoning errors often cause correct
answers to become incorrect, resulting in worse performances overall (Huang et
al., 2023). In this paper, we show that poor self-correction performance stems
from LLMs' inability to find logical mistakes, rather than their ability to
correct a known mistake. Firstly, we benchmark several state-of-the-art LLMs on
their mistake-finding ability and demonstrate that they generally struggle with
the task, even in highly objective, unambiguous cases. Secondly, we test the
correction abilities of LLMs -- separately from mistake finding -- using a
backtracking setup that feeds ground truth mistake location information to the
model. We show that this boosts downstream task performance across our 5
reasoning tasks, indicating that LLMs' correction abilities are robust.
Finally, we show that it is possible to obtain mistake location information
without ground truth labels or in-domain training data. We train a small
classifier with out-of-domain data, which exhibits stronger mistake-finding
performance than prompting a large model. We release our dataset of
LLM-generated logical mistakes, BIG-Bench Mistake, to enable further research
into locating LLM reasoning mistakes.
