Tracing Audio Grounding and Answer Selection in Audio LLMs

The paper investigates how Audio Large Language Models (Audio LLMs) actually use audio input to determine answers, rather than relying on textual cues. It finds that replacing audio with silence or unrelated audio degrades performance more after training than before, that acoustic information shapes representations in early-to-middle layers and influences final predictions in middle-to-late layers, and that training impacts specific layer bands most strongly. These observations offer a mechanistic view of how training enhances the use of acoustic evidence in Audio LLMs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms