@inproceedings{qiu-etal-2026-lst,
    title = "{LST} at {MWE}-2026 {A}d{MIR}e 2: Advancing Multimodal Idiomaticity Representation",
    author = "Qiu, Le  and
      Hsu, Yu-Yin  and
      Chersoni, Emmanuele",
    editor = {Ojha, Atul Kr.  and
      Mititelu, Verginica Barbu  and
      Constant, Mathieu  and
      Stoyanova, Ivelina  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.mwe-1.27/",
    pages = "203--207",
    ISBN = "979-8-89176-363-0",
    abstract = "This paper presents our methods for the AdMIRe 2.0 shared task, which addresses multilingual and multimodal idiom understanding. Our submission focuses on the text-only track. Specifically, we employ an ensemble of three large language models (LLMs) to directly perform the presented image ranking task. Each model independently produces a ranking of the candidate images, and we aggregate their outputs using a hard voting strategy to determine the final prediction. This ensemble learning framework leverages the complementary strengths of different LLMs, improving robustness and reducing the variance of individual model predictions."
}