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Chain-of-thought (CoT) prompting enables large language models (LLMs) tosolve complex reasoning tasks by generating an explanation before the finalprediction. Despite it's promising ability, a critical downside of CoTprompting is that the performance is greatly affected by the factuality of thegenerated explanation. To improve the correctness of the explanations,fine-tuning language models with explanation data is needed. However, thereexists only a few datasets that can be used for such approaches, and no datacollection tool for building them. Thus, we introduce CoTEVer, a tool-kit forannotating the factual correctness of generated explanations and collectingrevision data of wrong explanations. Furthermore, we suggest several use caseswhere the data collected with CoTEVer can be utilized for enhancing thefaithfulness of explanations. Our toolkit is publicly available athttps://github.com/SeungoneKim/CoTEVer.

CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification