Towards Dynamic Metaphor Identification: Evaluating GPT O-Series Models on Five Metaphoricity Cues in U.S. Trade Corpora

Abstract

Although recent advances have focused on detecting metaphors, existing models generally treat them as static entities. There has been little research into identifying dynamic metaphors in discourse. This article addresses this gap by focusing on metaphoricity cues: Linguistic signals that may indicate the activation of metaphoric meaning in different discourse contexts. This study examines the ability of OpenAI’s O-series models (O4-mini, O4-mini-high and O3) in detecting five metaphoricity cues in the U.S. trade discourse, including cues of explicit mapping, emphasis, marking, repetition and novelisation. Research results show that the models performed best on repetition and emphasis, while novelisation was the most difficult cue to detect.

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