  Chain-of-thought (CoT) prompting enables large language models (LLMs) to
solve complex reasoning tasks by generating an explanation before the final
prediction. Despite it's promising ability, a critical downside of CoT
prompting is that the performance is greatly affected by the factuality of the
generated explanation. To improve the correctness of the explanations,
fine-tuning language models with explanation data is needed. However, there
exists only a few datasets that can be used for such approaches, and no data
collection tool for building them. Thus, we introduce CoTEVer, a tool-kit for
annotating the factual correctness of generated explanations and collecting
revision data of wrong explanations. Furthermore, we suggest several use cases
where the data collected with CoTEVer can be utilized for enhancing the
faithfulness of explanations. Our toolkit is publicly available at
https://github.com/SeungoneKim/CoTEVer.
