Are Audio-Language Models Listening? Audio-Specialist Heads for Adaptive Audio Steering

• URL: http://arxiv.org/abs/2603.06854v1
• Date: Fri, 06 Mar 2026 20:18:30 GMT
• Title: Are Audio-Language Models Listening? Audio-Specialist Heads for Adaptive Audio Steering
• Abstract summary: Multimodal large language models can exhibit text dominance, over-relying on linguistic priors instead of grounding predictions in non-text inputs.<n>One example is large audio-language models (LALMs) where decisive audio evidence can be under-utilized even when it contains important information.<n>We show that this signal increases when audio evidence affects the model's output, providing an indicator of audio engagement under standard prompting.
• Score: 10.510226262026755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models can exhibit text dominance, over-relying on linguistic priors instead of grounding predictions in non-text inputs. One example is large audio-language models (LALMs) where decisive audio evidence can be under-utilized even when it contains important information. To address this issue we use mechanistic interpretability to identify a small set of audio-specialist attention heads whose audio attention yields a ``listening'' signal. We show that this signal increases when audio evidence affects the model's output, providing an indicator of audio engagement under standard prompting. Leveraging this localization, we construct an audio--silence steering direction and apply an inference-time activation intervention to the final representation, amplifying the model's audio effect. To demonstrate the utility of this intervention, we show on MMAU that this improves accuracy by up to +8.0 percentage points on two Qwen-based LALMs, without any parameter updates.

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