ACL 2025finding0 citations
Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?
Simeon Junker, Manar Ali, Larissa Koch, Sina Zarrieß, Hendrik Buschmeier
Abstract
We investigate the linguistic abilities of multimodal large language models in reference resolution tasks featuring simple yet abstract visual stimuli, such as color patches and color grids. Although the task may not seem challenging for today’s language models, being straightforward for human dyads, we consider it to be a highly relevant probe of the pragmatic capabilities of MLLMs. Our results and analyses indeed suggest that basic pragmatic capabilities, such as context-dependent interpretation of color descriptions, still constitute major challenges for state-of-the-art MLLMs.
BibTeX
@inproceedings{junker-etal-2025-multimodal,
title = "Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?",
author = "Junker, Simeon and
Ali, Manar and
Koch, Larissa and
Zarrie{\ss}, Sina and
Buschmeier, Hendrik",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1236/",
doi = "10.18653/v1/2025.findings-acl.1236",
pages = "24101--24109",
ISBN = "979-8-89176-256-5"
}