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。

CoTEVer: 用于解释验证的思路链提示注释工具包

CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification

Many methods now exist for conditioning model outputs on task instructions,
retrieved documents, and user-provided explanations and feedback. Rather than
relying solely on examples of task inputs and outputs, these approaches use
valuable additional data for improving model correctness and aligning learned
models with human priors. Meanwhile, a growing body of evidence suggests that
some language models can (1) store a large amount of knowledge in their
parameters, and (2) perform inference over tasks in textual inputs at test
time. These results raise the possibility that, for some tasks, humans cannot
explain to a model any more about the task than it already knows or could infer
on its own. In this paper, we study the circumstances under which explanations
of individual data points can (or cannot) improve modeling performance. In
order to carefully control important properties of the data and explanations,
we introduce a synthetic dataset for experiments, and we also make use of three
existing datasets with explanations: e-SNLI, TACRED, and SemEval. We first give
a formal framework for the available modeling approaches, in which explanation
data can be used as model inputs, as targets, or as a prior. After arguing that
the most promising role for explanation data is as model inputs, we propose to
use a retrieval-based method and show that it solves our synthetic task with
accuracies upwards of 95%, while baselines without explanation data achieve
below 65% accuracy. We then identify properties of datasets for which
retrieval-based modeling fails. With the three existing datasets, we find no
improvements from explanation retrieval. Drawing on findings from our synthetic
task, we suggest that at least one of six preconditions for successful modeling
fails to hold with these datasets. Our code is publicly available at
this https URL

这篇论文研究了解释数据对于模型效能的影响，利用回收法证明了推理为主的文本输入任务中，解释数据可用作模型输入，且采用检索式的建模方法可以使合成任务的准确率达到 95%，而无解释数据的基线准确率低于 65%。