@inproceedings{kunz-kuhlmann-2024-properties,
    title = "Properties and Challenges of {LLM}-Generated Explanations",
    author = "Kunz, Jenny  and
      Kuhlmann, Marco",
    editor = "Blodgett, Su Lin  and
      Curry, Amanda Cercas  and
      Dey, Sunipa  and
      Madaio, Michael  and
      Nenkova, Ani  and
      Yang, Diyi  and
      Xiao, Ziang",
    booktitle = "Proceedings of the Third Workshop on Bridging Human--Computer Interaction and Natural Language Processing",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.hcinlp-1.2",
    pages = "13--27",
    abstract = "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.",
}