@inproceedings{sengupta-etal-2025-investigating,
    title = "Investigating the Impact of Conceptual Metaphors on {LLM}-based {NLI} through Shapley Interactions",
    author = {Sengupta, Meghdut  and
      Muschalik, Maximilian  and
      Fumagalli, Fabian  and
      Hammer, Barbara  and
      H{\"u}llermeier, Eyke  and
      Ghosh, Debanjan  and
      Wachsmuth, Henning},
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.942/",
    doi = "10.18653/v1/2025.findings-emnlp.942",
    pages = "17393--17403",
    ISBN = "979-8-89176-335-7",
    abstract = "Metaphorical language is prevalent in everyday communication, often used unconsciously, as in ``rising crime.'' While LLMs excel at identifying metaphors in text, they struggle with downstream tasks that implicitly require correct metaphor interpretation, such as natural language inference (NLI). This work explores how LLMs perform on NLI with metaphorical input. Particularly, we investigate whether incorporating conceptual metaphors (source and target domains) enhances performance in zero-shot and few-shot settings. Our contributions are two-fold: (1) we extend metaphorical texts in an existing NLI dataset by source and target domains, and (2) we conduct an ablation study using Shapley values and interactions to assess the extent to which LLMs interpret metaphorical language correctly in NLI. Our results indicate that incorporating conceptual metaphors often improves task performance."
}