Natural language is among the most accessible tools for explaining decisions to humans, and large pretrained language models (PLMs) have demonstrated impressive abilities to generate coherent natural language explanations (NLE). The existing NLE research perspectives do not take the audience into account. An NLE can have high textual quality, but it might not accommodate audiences' needs and preference. To address this limitation, we propose an alternative perspective, situated NLE, including a situated generation framework and a situated evaluation framework. On the generation side, we propose simple prompt engineering methods that adapt the NLEs to situations. In human studies, the annotators preferred the situated NLEs. On the evaluation side, we set up automated evaluation scores in lexical, semantic, and pragmatic categories. The scores can be used to select the most suitable prompts to generate NLEs. Situated NLE provides a perspective to conduct further research on automatic NLE generations.

翻译：自然语言是向人类解释决策最易用的工具之一，而大型预训练语言模型（PLMs）在生成连贯的自然语言解释（NLE）方面已展现出令人瞩目的能力。现有的NLE研究视角并未考虑受众因素。一则NLE可能具有较高的文本质量，但未必能契合受众的需求与偏好。为解决这一局限，我们提出一种替代性视角——情境化NLE，包括情境化生成框架与情境化评估框架。在生成层面，我们提出简单的提示工程方法，使NLE能够适应具体情境。人工研究显示，标注者更偏好情境化NLE。在评估层面，我们建立了涵盖词汇、语义和语用类别的自动化评估分数。这些分数可用于筛选最合适的提示以生成NLE。情境化NLE为自动NLE生成的进一步研究提供了新的视角。