@inproceedings{cong-rayz-2026-artificial,
    title = "Artificial Language Learning Paradigm Reveals Pragmatic Blind Spots in Vision-Language Models",
    author = "Cong, Yan  and
      Rayz, Julia",
    editor = "Bernard, Timoth{\'e}e  and
      Chersoni, Emmanuele  and
      Rambelli, Giulia",
    booktitle = "Proceedings of the Third Workshop on the Bridges and Gaps between Formal and Computational Linguistics ({B}ri{G}ap-3)",
    month = jul,
    year = "2026",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-brigap/2026.brigap-1.6/",
    pages = "50--62",
    ISBN = "XXX-X-XXXXXX-XX-X",
    abstract = "Humans are pragmatic language users who naturally and effortlessly reason about the choice of utterances that help collaborate and engage in social interactions. In this paper, we examine whether vision-language models (VLMs) exhibit similar pragmatic reasoning effects through a validated artificial language learning paradigm. Across four experiments, we evaluate five VLMs' sensitivity to production cost, ambiguity-driven competition effects, and the influences of visual features and model properties. We find evidence of cost effects in some VLMs. However, no model consistently exhibits competition effects driven by ambiguity risk, a hallmark of Gricean pragmatic reasoning. We also find that model scale alone does not predict pragmatic alignment; architectural choices play a larger role. Moreover, probability-based methods reveal clearer effects than prompting. Overall, current VLMs capture only a restricted subset of pragmatic effects central to Gricean reasoning, suggesting gaps in multimodal pragmatic reasoning."
}