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, \textit{situated} NLE. On the evaluation side, we set up automated evaluation scores. These scores describe the properties of NLEs in lexical, semantic, and pragmatic categories. On the generation side, we identify three prompt engineering techniques and assess their applicability on the situations. Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.

翻译：自然语言是向人类解释决策时最易获取的工具之一，大型预训练语言模型（PLM）已展现出生成连贯自然语言解释（NLE）的卓越能力。现有NLE研究视角未充分考虑受众因素：一则NLE可能具备较高的文本质量，但未必契合受众的需求与偏好。为弥补这一不足，我们提出替代性视角——情境化NLE。在评估层面，我们构建了自动化评估指标，从词汇、语义及语用维度刻画NLE属性；在生成层面，我们识别出三种提示工程技术，并评估其在不同情境下的适用性。情境化NLE为解释的生成与评估提供了新视角，有助于推动该领域的后续研究。