@inproceedings{blackmore-stone-2025-llms,
    title = "Can {LLM}s Disambiguate Grounded Language? The Case of {PP} Attachment",
    author = "Blackmore, John  and
      Stone, Matthew",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.20/",
    pages = "166--174",
    abstract = "We explore the potential of large language models in resolving ambiguity in prepositional phrase attachments in grounded language. We find that when prompted in such a way that we can compute a probability of the respective attachment, models yield promising results. However, additional inputs from a measure of information structure may help improve prediction accuracy. We also investigate where we need more sophisticated tools, commonsense reasoning, world knowledge, and additional context to resolve ambiguity."
}