Steering Where to Listen: Instruction-Based Activation Steering Redirects Temporal Attention in Large Audio-Language Models
arXiv:2606.11400v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) excel at audio understanding but expose little about where in an audio signal they attend. We introduce instruction-based vector steering, which constructs a steering vector by contrasting activations from differently instructed prompts while keeping the audio fixed. Through a systematic probe of LALM attention, we find that - unlike standard prompting or audio-based steering - this intervention significantly redistributes the temporal attention allocated to audio tokens, concentrating it on acoustically rele
The rapid advancement in large language models is extending into audio, necessitating new methods to understand and control their internal workings and attention mechanisms.
This development offers a novel method to fine-tune AI attention, making Large Audio-Language Models more interpretable and controllable for specific tasks, enhancing their utility and reliability.
Researchers can now steer the temporal attention of LALMs using instruction-based prompts, leading to more precise and controllable audio analysis rather than relying solely on raw audio input.
- · AI developers
- · Audio analysis platforms
- · Researchers in interpretability
- · Speech recognition applications
- · Developers of less precise audio AI systems
- · Systems with opaque AI attention
Instruction-based steering allows for more targeted and efficient use of LALMs in various audio processing applications.
Improved interpretability and control of LALMs could accelerate their adoption in sensitive applications like security or diagnostics.
This method might generalize to other multimodal AI systems, leading to a broader paradigm shift in AI control and interaction.
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Read at arXiv cs.AI