AudioScorer: Learning Question-Aware Token Relevance for Large Audio Language Models
Large Audio Language Models (LALMs) reason over speech, environmental sounds, music, and other acoustic information. However, their audio encoders produce dense token sequences that make long-form audio costly to process. Existing audio pruning methods reduce this cost using heuristics such as attention, representation similarity, or temporal sampling, but do not explicitly learn which tokens are most useful for a given question. We propose AudioScorer, a lightweight model that scores audio tokens according to the user question. It is trained by distilling the ranking of audio tokens under the frozen LALM's answer-gradient saliency, together with downstream answer supervision. Compared wi...
| Authors | Marwan Farag, Bin Yang |
|---|---|
| Primary category | cs.SD |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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