Large language models (LLMs) are proficient at generating fluent text with
minimal task-specific supervision. Yet, their ability to provide well-grounded
rationalizations for knowledge-intensive tasks remains under-explored. Such
tasks, like commonsense multiple-choice questions, require rationales based on
world knowledge to support predictions and refute alternate options. We
consider the task of generating knowledge-guided rationalization in natural
language by using expert-written examples in a few-shot manner. Surprisingly,
crowd-workers preferred knowledge-grounded rationales over crowdsourced
rationalizations, citing their factuality, sufficiency, and comprehensive
refutations. Although LLMs-generated rationales were preferable, further
improvements in conciseness and novelty are required. In another study, we show
how rationalization of incorrect model predictions erodes humans' trust in
LLM-generated rationales. Motivated by these observations, we create a
two-stage pipeline to review task predictions and eliminate potential incorrect
decisions before rationalization, enabling trustworthy rationale generation.

大型语言模型能够在缺乏任务特定监督的情况下生成流畅的文本，但其在具有知识密集型任务中提供有根据的解释的能力仍未得到充分探索。我们通过使用专家编写的几个样本，以少量样本的方式生成基于知识的解释来解决这一问题。通过研究发现，众包工作者更喜欢基于知识的解释，因为它们具有实际性、充分性和全面的反驳。虽然大型语言模型生成的解释更受欢迎，但需要进一步提高简明性和新颖性。另外，我们还展示了错误模型预测的解释如何削弱人类对大型语言模型生成的解释的信任。基于这些观察的动机，我们创建了一个两阶段的流程，在生成解释之前审查任务预测并消除潜在的错误决策，以实现可信赖的解释生成。