Learning to Steer: Input-dependent Steering for Multimodal LLMs

Jayneel Parekh, Pegah KHAYATAN, Mustafa Shukor, Arnaud Dapogny, Alasdair Newson, Matthieu Cord

Keywords: multimodal large language models, large multimodal models, steering, representation steering, safety alignment, hallucinations, vision-language models

TL;DR: Perform input dependent steering of MLLMs by learning to predict steering vectors from input context

Abstract:

Steering has emerged as a practical approach to enable post-hoc guidance of LLMs towards enforcing a specific behavior.
However, it remains largely underexplored for multimodal LLMs (MLLMs); furthermore, existing steering techniques, such as \textit{mean} steering, rely on a single steering vector, applied independently of the input query. This paradigm faces limitations when the desired behavior is dependent on the example at hand. For example, a safe answer may consist in abstaining from answering when asked for an illegal activity, or may point to external resources or consultation with an expert when asked about medical advice. In this paper, we investigate a fine-grained steering that uses an input-specific linear shift. This shift is computed using contrastive input-specific prompting. However, the input-specific prompts required for this approach are not known at test time. Therefore, we propose to train a small auxiliary module to predict the input-specific steering vector. Our approach, dubbed as L2S (Learn-to-Steer), demonstrates that it reduces hallucinations and enforces safety in MLLMs, outperforming other static baselines. We will open-source our code.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 7290