The self-rationalising capabilities of large language models (LLMs) have been explored in restricted settings, using task/specific data sets. However, current LLMs do not (only) rely on specifically annotated data; nonetheless, they frequently explain their outputs. The properties of the generated explanations are influenced by the pre-training corpus and by the target data used for instruction fine-tuning. As the pre-training corpus includes a large amount of human-written explanations "in the wild", we hypothesise that LLMs adopt common properties of human explanations. By analysing the outputs for a multi-domain instruction fine-tuning data set, we find that generated explanations show selectivity and contain illustrative elements, but less frequently are subjective or misleading. We discuss reasons and consequences of the properties' presence or absence. In particular, we outline positive and negative implications depending on the goals and user groups of the self-rationalising system.

翻译：大语言模型（LLM）的自我解释能力已在受限场景中通过特定任务数据集得到探索。然而，当前LLM并非（仅）依赖特定标注数据，却仍频繁对其输出进行解释。生成的解释性质受到预训练语料库及指令微调所用目标数据的影响。由于预训练语料库包含大量“野生的”人类撰写的解释，我们假设LLM会采纳人类解释的常见特性。通过分析多领域指令微调数据集的输出，我们发现生成的解释具有选择性，并包含说明性元素，但较少出现主观性或误导性内容。我们探讨了这些特性存在或缺位的原因及后果，并特别根据自我解释系统的目标与用户群体，概括了其积极与消极影响。