Can Audio LLMs Understand Prosodic Focus? An Alternative-Semantics Inference Test with "Only"

Daniel Hardt


Abstract
We introduce a new inference test for audio LLMs, where the correct response crucially depends on the location of a focal accent. The proposed task shows for the first time how to test the ability of LLMs to incorporate audio information in truth-conditional semantic interpretation, relying on the theory of alternative semantics. Several native audio models are tested, with performance ranging from random guessing to around 90% accuracy. The task is challenging for all the models tested. A detailed error analysis reveals specific areas of difficulty. The theory of alternative semantics provides a basis for a wide range of systematic tests of audio models.
Anthology ID:
2026.findings-emnlp.1884
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2026
Month:
October
Year:
2026
Address:
Budapest, Hungary
Editors:
Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
40166–40183
Language:
URL:
https://aclanthology.org/2026.findings-emnlp.1884/
DOI:
10.18653/2026.findings-emnlp.1884
Bibkey:
Cite (ACL):
Daniel Hardt. 2026. Can Audio LLMs Understand Prosodic Focus? An Alternative-Semantics Inference Test with "Only". In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 40166–40183, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):
Can Audio LLMs Understand Prosodic Focus? An Alternative-Semantics Inference Test with “Only” (Hardt, Findings 2026)
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PDF:
https://aclanthology.org/2026.findings-emnlp.1884.pdf