Tracing Audio Grounding and Answer Selection in Audio LLMs
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
arXiv:2609.04637 (cs)
[Submitted on 4 Sep 2026]
Title:Tracing Audio Grounding and Answer Selection in Audio LLMs
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Abstract:Audio Large Language Models (Audio LLMs) have advanced in audio understanding, yet they can still predict the answer by reasoning from textual cues or linguistic priors rather than the provided audio. A common remedy is to train models on data whose answers cannot be inferred from text alone. This approach can improve performance, but what changes within the model remains unclear. In this paper, we ask what must happen inside the model for the audio to actually determine the answer. Our findings are threefold. (1) Replacing the audio with silence or unrelated audio causes substantially larger performance degradation in the trained model than in the pretrained model. (2) Acoustic information most strongly shapes the model's representations of the answer choices in early-to-middle layers, while training mainly increases the influence of audio information on the final prediction in middle-to-late layers. (3) The weights learned during training have their largest impact in specific layer bands. Together, these results provide a mechanistic account of how training strengthens the use of acoustic evidence in Audio LLMs.
| Comments: | Preprint |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD) |
| Cite as: | arXiv:2609.04637 [cs.CL] |
| (or arXiv:2609.04637v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.04637
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Tracing Audio Grounding and Answer Selection in Audio LLMs, by Hyebin Cho and 3 other authors
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