Can LLMs Disambiguate Grounded Language? The Case of PP Attachment

John Blackmore, Matthew Stone


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.
Anthology ID:
2025.ranlp-1.20
Volume:
Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
Month:
September
Year:
2025
Address:
Varna, Bulgaria
Editors:
Galia Angelova, Maria Kunilovskaya, Marie Escribe, Ruslan Mitkov
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:
166–174
Language:
URL:
https://aclanthology.org/2025.ranlp-1.20/
DOI:
Bibkey:
Cite (ACL):
John Blackmore and Matthew Stone. 2025. Can LLMs Disambiguate Grounded Language? The Case of PP Attachment. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 166–174, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
Can LLMs Disambiguate Grounded Language? The Case of PP Attachment (Blackmore & Stone, RANLP 2025)
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PDF:
https://aclanthology.org/2025.ranlp-1.20.pdf