AnchorPrompt: Self-Distilled Soft Prompts for Robust Audio-Language Models
Large audio-language models (LALMs) are sensitive to input perturbations, such as noise, waveform corruption, and adversarial injections. We propose AnchorPrompt, an efficient adaptation method that keeps the model frozen and learns a single block of prompt vectors inserted at the decoder input, between the audio and question embeddings. We train these vectors through self-distillation over diverse audio and text perturbations. To improve answer consistency and mitigate hallucination, we use the model's prediction on the clean recording as the target for answerable inputs, and assign a refusal target when the audio lacks sufficient evidence to answer. Furthermore, AnchorPrompt is perturba...
| Authors | Pooneh Mousavi, Amir Ivry, Mirco Ravanelli et al. |
|---|---|
| Primary category | cs.SD |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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