Title: Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
Primary area: interpretability and explainable AI
Keywords: steering vectors, multimodal large language models, interpretability

Abstract: Steering methods have emerged as effective tools for guiding large language models’ behavior, yet multimodal large language models (MLLMs) lack comparable techniques due to recency and architectural diversity. Inspired by this gap, we demonstrate that steering vectors derived solely from text-only LLM backbones can effectively guide their multimodal counterparts, revealing a novel cross-modal transfer that enables reuse of existing interpretability tools. Using community-standard methods—Sparse Autoencoders (SAE), Mean Shift, and Linear Probing—we systematically validate this transfer effect across diverse MLLM architectures and visual reasoning tasks. Text-derived steering consistently enhances multimodal performance, with mean shift achieving up to +7.3% improvement in spatial relationship accuracy and +3.3% in counting accuracy on CV-Bench, and exhibits strong generalization to out-of-distribution datasets. These results highlight textual steering vectors as a powerful, efficient mechanism for enhancing grounding in MLLMs with minimal additional data collection and computational overhead.

TL;DR: textual steering vectors can steer multimodal LLMs and improve their visual understanding