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Nudging Hidden States: Training-Free Model Steering for Chain-of-Thought Reasoning in Large Audio-Language Models
arXiv – CS AI|Lok-Lam Ieong, Chia-Chien Chen, Chih-Kai Yang, Yu-Han Huang, An-Yu Cheng, Hung-yi Lee|
🤖AI Summary
Researchers developed training-free model steering techniques to improve reasoning in large audio-language models (LALMs) through chain-of-thought prompting. The approach achieved up to 4.4% accuracy gains and demonstrated cross-modal transfer where text-derived steering vectors can effectively guide speech-based reasoning.
Key Takeaways
- →Training-free model steering improves LALM reasoning performance by up to 4.4% over standard chain-of-thought prompting.
- →Cross-modal transfer enables text-derived steering vectors to effectively guide speech-based reasoning tasks.
- →The approach demonstrates high data efficiency, requiring only few text samples to create effective steering vectors.
- →Three steering strategies were evaluated across four LALMs and four benchmarks, showing general applicability.
- →Hyperparameter sensitivity analysis revealed the robustness of these steering approaches for practical deployment.
#audio-language-models#chain-of-thought#model-steering#cross-modal-transfer#inference-optimization#training-free#reasoning-improvement#speech-processing
Read Original →via arXiv – CS AI
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