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Learning to Steer: Input-dependent Steering for Multimodal LLMs

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

Learning to Steer: Input-dependent Steering for Multimodal LLMs

The article "Learning to Steer: Input-dependent Steering for Multimodal LLMs" addresses the challenges in guiding the behavior of multimodal large language models (LLMs). It highlights the limitations of current steering methods, which often apply uniform adjustments regardless of the input context (F2). To overcome these limitations, the article proposes an input-dependent steering approach that adapts based on specific examples (F1). This tailored method aims to enhance the models' responsiveness and accuracy by considering the unique characteristics of each input. The suggested approach is positively evaluated for its potential effectiveness in improving model behavior (A1). By dynamically adjusting steering strategies, the technique promises to offer more nuanced control over multimodal LLM outputs (F3). This development marks a significant step toward more flexible and context-aware AI systems.

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