@inproceedings{reimann-scheffler-2025-using,
title = "Using Large Language Models to Perform {MIPVU}-Inspired Automatic Metaphor Detection",
author = "Reimann, Sebastian and
Scheffler, Tatjana",
editor = "Rambelli, Giulia and
Ilievski, Filip and
Bolognesi, Marianna and
Sommerauer, Pia",
booktitle = "Proceedings of the 2nd Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-Angle II)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.analogyangle-1.2/",
doi = "10.18653/v1/2025.analogyangle-1.2",
pages = "10--21",
ISBN = "979-8-89176-274-9",
abstract = "Automatic metaphor detection has often been inspired by linguistic procedures for manual metaphor identification. In this work, we test how closely the steps required by the Metaphor Identification Procedure VU Amsterdam (MIPVU) can be translated into prompts for generative Large Language Models (LLMs) and how well three commonly used LLMs are able to perform these steps. We find that while the procedure itself can be modeled with only a few compromises, neither language model is able to match the performance of supervised, fine-tuned methods for metaphor detection. All models failed to sufficiently filter out literal examples, where no contrast between the contextual and a more basic or concrete meaning was present. Both versions of LLaMa however signaled interesting potentials in detecting similarities between literal and metaphoric meanings that may be exploited in further work."
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<abstract>Automatic metaphor detection has often been inspired by linguistic procedures for manual metaphor identification. In this work, we test how closely the steps required by the Metaphor Identification Procedure VU Amsterdam (MIPVU) can be translated into prompts for generative Large Language Models (LLMs) and how well three commonly used LLMs are able to perform these steps. We find that while the procedure itself can be modeled with only a few compromises, neither language model is able to match the performance of supervised, fine-tuned methods for metaphor detection. All models failed to sufficiently filter out literal examples, where no contrast between the contextual and a more basic or concrete meaning was present. Both versions of LLaMa however signaled interesting potentials in detecting similarities between literal and metaphoric meanings that may be exploited in further work.</abstract>
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%0 Conference Proceedings
%T Using Large Language Models to Perform MIPVU-Inspired Automatic Metaphor Detection
%A Reimann, Sebastian
%A Scheffler, Tatjana
%Y Rambelli, Giulia
%Y Ilievski, Filip
%Y Bolognesi, Marianna
%Y Sommerauer, Pia
%S Proceedings of the 2nd Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-Angle II)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-274-9
%F reimann-scheffler-2025-using
%X Automatic metaphor detection has often been inspired by linguistic procedures for manual metaphor identification. In this work, we test how closely the steps required by the Metaphor Identification Procedure VU Amsterdam (MIPVU) can be translated into prompts for generative Large Language Models (LLMs) and how well three commonly used LLMs are able to perform these steps. We find that while the procedure itself can be modeled with only a few compromises, neither language model is able to match the performance of supervised, fine-tuned methods for metaphor detection. All models failed to sufficiently filter out literal examples, where no contrast between the contextual and a more basic or concrete meaning was present. Both versions of LLaMa however signaled interesting potentials in detecting similarities between literal and metaphoric meanings that may be exploited in further work.
%R 10.18653/v1/2025.analogyangle-1.2
%U https://aclanthology.org/2025.analogyangle-1.2/
%U https://doi.org/10.18653/v1/2025.analogyangle-1.2
%P 10-21
Markdown (Informal)
[Using Large Language Models to Perform MIPVU-Inspired Automatic Metaphor Detection](https://aclanthology.org/2025.analogyangle-1.2/) (Reimann & Scheffler, Analogy-Angle 2025)
ACL