@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions
%A Sengupta, Meghdut
%A Muschalik, Maximilian
%A Fumagalli, Fabian
%A Hammer, Barbara
%A Hüllermeier, Eyke
%A Ghosh, Debanjan
%A Wachsmuth, Henning
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F sengupta-etal-2025-investigating
%X 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.
%R 10.18653/v1/2025.findings-emnlp.942
%U https://aclanthology.org/2025.findings-emnlp.942/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.942
%P 17393-17403
Markdown (Informal)
[Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions](https://aclanthology.org/2025.findings-emnlp.942/) (Sengupta et al., Findings 2025)
ACL