Evaluating and Steering Modality Preferences in Multimodal Large
  Language Model
Authors: name: Yu Zhang, name: Jinlong Ma, name: Yongshuai Hou, name: Xuefeng Bai, name: Kehai Chen, name: Yang Xiang, name: Jun Yu, name: Min Zhang

Multimodal large language models (MLLMs) have achieved remarkable performance
on complex tasks with multimodal context. However, it is still understudied
whether they exhibit modality preference when processing multimodal contexts.
To study this question, we first build a MC\textsuperscript{2}
benchmark under controlled evidence conflict scenarios to systematically
evaluate modality preference, which is the tendency to favor one modality over
another when making decisions based on multimodal conflicting evidence. Our
extensive evaluation reveals that all 18 tested MLLMs generally demonstrate
clear modality bias, and modality preference can be influenced by external
interventions. An in-depth analysis reveals that the preference direction can
be captured within the latent representations of MLLMs. Built on this, we
propose a probing and steering method based on representation engineering to
explicitly control modality preference without additional fine-tuning or
carefully crafted prompts. Our method effectively amplifies modality preference
toward a desired direction and applies to downstream tasks such as
hallucination mitigation and multimodal machine translation, yielding promising
improvements.