@inproceedings{xiang-etal-2025-evaluating,
    title = "Evaluating Instructively Generated Statement by Large Language Models for Directional Event Causality Identification",
    author = "Xiang, Wei  and
      Zhan, Chuanhong  and
      Zhang, Qing  and
      Wang, Bang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.43/",
    pages = "779--785",
    ISBN = "979-8-89176-256-5",
    abstract = "This paper aims to identify directional causal relations between events, including the existence and direction of causality. Previous studies mainly adopt prompt learning paradigm to predict a causal answer word based on a Pre-trained Language Model (PLM) for causality existence identification. However, the indecision in selecting answer words from some synonyms and the confusion of indicating opposite causal directions with the same answer word raise more challenges in directional causality identification. Inspired by the strong capabilities of pre-trained Generative Language Models (GLMs) in generating responses or statements, we propose to instruct a GLM to generate causality statements and identify directional event causality by evaluating the generated statements. Specifically, we propose an Instructive Generation and Statement Evaluation method to identify both the existence and direction of causality. We first fine-tune a GLM to instructively generate causality statements based on event description inputs. Then, we evaluate the rationality of the generated statements to determine the existence and direction of event causalities. Experiments on the ESC and MAVEN datasets show that our method significantly outperforms state-of-the-art algorithms, even with fewer training data."
}