@article{bas-etal-2026-towards,
    title = "Towards Dynamic Metaphor Identification: Evaluating {GPT} {O}-Series Models on Five Metaphoricity Cues in {U}.{S}. Trade Corpora",
    author = "Bas, Berkay  and
      Bloem, Jelke  and
      Tan, Xiaojuan",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.357/",
    pages = "4547--4559",
    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."
}