Nudging Hidden States: Training-Free Model Steering for Chain-of-Thought Reasoning in Large Audio-Language Models
Chain-of-thought (CoT) prompting has been extended to large audio-language models (LALMs) to elicit reasoning, yet enhancing its effectiveness without training remains challenging. We study inference-time model steering as a training-free approach to improve LALM reasoning. We introduce three strategies using diverse information sources and evaluate them across four LALMs and four benchmarks. Results show general accuracy gains up to 4.4% over CoT prompting. Notably, we identify a cross-modal transfer where steering vectors derived from few text samples effectively guide speech-based reasoning, demonstrating high data efficiency. We also examine hyperparameter sensitivity to understand the robustness of these approaches. Our findings position model steering as a practical direction for strengthening LALM reasoning.
69c648b42de2ee20094ede6a
Lok-Lam Ieong
69c648b42de2ee20094ede6b
Chia-Chien Chen
69c648b42de2ee20094ede6c
Chih-Kai Yang
claimed_verified
2026-03-27T11:32:34.247Z
646fa3016441111304fec68d
/avatars/923629340f3785ae8c6e52cf3674d5c2.svg
Chih-Kai Yang
zenyn
user
zenyn
69c648b42de2ee20094ede6d
Yu-Han Huang
69c648b42de2ee20094ede6e
An-Yu Cheng
69c648b42de2ee20094ede6f
Hung-yi Lee
Nudging Hidden States: Training-Free Model Steering for Chain-of-Thought Reasoning in Large Audio-Language Models
Chain-of-thought (CoT) prompting has been extended to large audio-language models (LALMs) to elicit reasoning, yet enhancing its effectiveness without training remains challenging. We study inference-time model steering as a training-free approach to improve LALM reasoning. We introduce three strategies using diverse information sources and evaluate them across four LALMs and four benchmarks. Results show general accuracy gains up to 4.4% over CoT prompting. Notably, we identify a cross-modal transfer where steering vectors derived from few text samples effectively guide speech-based reasoning, demonstrating high data efficiency. We also examine hyperparameter sensitivity to understand the robustness of these approaches. Our findings position model steering as a practical direction for strengthening LALM reasoning.
Training-free inference-time model steering enhances large audio-language model reasoning through diverse information sources and cross-modal transfer from text to speech, achieving improved accuracy and data efficiency.
chain-of-thought prompting large audio-language models inference-time model steering steering vectors cross-modal transfer few-shot learning