@inproceedings{spiliopoulou-etal-2022-events,
    title = "{E}v{E}nt{S} {R}ea{LM}: Event Reasoning of Entity States via Language Models",
    author = "Spiliopoulou, Evangelia  and
      Pagnoni, Artidoro  and
      Bisk, Yonatan  and
      Hovy, Eduard",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2022.emnlp-main.129/",
    doi = "10.18653/v1/2022.emnlp-main.129",
    pages = "1982--1997",
    abstract = "This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail to reason about the world. Conversely, we also demonstrate that existing approaches often misrepresent the surprising abilities of LLMs via improper task encodings and that proper model prompting can dramatically improve performance of reported baseline results across multiple tasks. In particular, our results indicate that our prompting technique is especially useful for unseen attributes (out-of-domain) or when only limited data is available."
}