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
this https URL.

该研究提出了一种利用 CoT 提示的方法，可以通过生成解释来解决复杂的推理任务，但其性能受到生成解释的事实性的影响。为了改进生成解释的正确性，需要使用解释数据来微调语言模型。研究者使用 CoTEVer 工具箱为生成的解释注释事实上的正确性，并收集错误解释的修订数据。他们还提出了几种利用 CoTEVer 收集的数据来增强解释忠实度的用例。