@inproceedings{hardt-2026-audio,
title = "Can Audio {LLM}s Understand Prosodic Focus? An Alternative-Semantics Inference Test with ``Only''",
author = "Hardt, Daniel",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1884/",
doi = "10.18653/2026.findings-emnlp.1884",
pages = "40166--40183",
ISBN = "979-8-89176-462-0",
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."
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%0 Conference Proceedings
%T Can Audio LLMs Understand Prosodic Focus? An Alternative-Semantics Inference Test with “Only”
%A Hardt, Daniel
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hardt-2026-audio
%X 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.
%R 10.18653/2026.findings-emnlp.1884
%U https://aclanthology.org/2026.findings-emnlp.1884/
%U https://doi.org/10.18653/2026.findings-emnlp.1884
%P 40166-40183
Markdown (Informal)
[Can Audio LLMs Understand Prosodic Focus? An Alternative-Semantics Inference Test with "Only"](https://aclanthology.org/2026.findings-emnlp.1884/) (Hardt, Findings 2026)
ACL