Using Large Language Models to Perform MIPVU-Inspired Automatic Metaphor Detection

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.