Source-Modality Monitoring in Vision-Language Models
Etha Tianze Hua, Tian Yun, Ellie Pavlick
Why It Matters
What makes this one worth your time
Understanding source-modality monitoring can enhance the robustness and reliability of multimodal systems, which are increasingly prevalent in AI applications.
The paper investigates how vision-language models track and bind input sources using syntactic and semantic signals.
Summary
The paper introduces and explores the concept of source-modality monitoring in multimodal models, specifically focusing on vision-language models. It examines how these models use syntactic and semantic signals to bind words in prompts to specific input sources, such as images. The study finds that semantic signals are more influential than syntactic ones when modalities are distributionally distinct, and discusses the implications for model robustness and multimodal systems.
Key contributions
- Introduction of the concept of source-modality monitoring in multimodal models.
- Evaluation of syntactic versus semantic signal use in vision-language models.
- Discussion of implications for model robustness and multimodal systems.
Notable insights
- Semantic signals are more influential than syntactic signals in binding tasks when modalities are distinct.
- The study frames source-modality monitoring as part of the broader binding problem in AI.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2604.22038v1 Announce Type: new Abstract: We define and investigate source-modality monitoring -- the ability of multimodal models to track and communicate the input source from which pieces of information originate. We consider source-modality monitoring as an instance of the more general binding problem, and evaluate the extent to which models exploit syntactic vs. semantic signals in order to bind words like image in a user-provided prompt to specific components of their input and context (i.e., actual images). Across experiments spanning 11 vision-language models (VLMs) performing target-modality information retrieval tasks, we find that both syntactic and semantic signals play an important role, but that the latter tend to outweigh the former in cases when modalities are highly distinct distributionally. We discuss the implications of these findings for model robustness, and in the context of increasingly multimodal agentic systems.