@inproceedings{alexeeva-etal-2022-combining,
    title = "Combining Extraction and Generation for Constructing Belief-Consequence Causal Links",
    author = "Alexeeva, Maria  and
      Beal Cohen, Allegra A.  and
      Surdeanu, Mihai",
    editor = "Tafreshi, Shabnam  and
      Sedoc, Jo{\~a}o  and
      Rogers, Anna  and
      Drozd, Aleksandr  and
      Rumshisky, Anna  and
      Akula, Arjun",
    booktitle = "Proceedings of the Third Workshop on Insights from Negative Results in NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.insights-1.22/",
    doi = "10.18653/v1/2022.insights-1.22",
    pages = "159--164",
    abstract = "In this paper, we introduce and justify a new task{---}causal link extraction based on beliefs{---}and do a qualitative analysis of the ability of a large language model{---}InstructGPT-3{---}to generate implicit consequences of beliefs. With the language model-generated consequences being promising, but not consistent, we propose directions of future work, including data collection, explicit consequence extraction using rule-based and language modeling-based approaches, and using explicitly stated consequences of beliefs to fine-tune or prompt the language model to produce outputs suitable for the task."
}