Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition
arXiv:2606.00959v1 Announce Type: new Abstract: Understanding modality interaction in multimodal large language models (MLLMs) is central to reliable deployment. We introduce Partial Information Decomposition (PID) as a decision-level framework that separates unique, redundant, and synergistic contributions of sensory and linguistic inputs, beyond representation alignment and outcome-based evaluation. Across vision--language benchmarks, PID reveals recurring modality-use profiles: reasoning and grounding-oriented tasks tend to exhibit high synergy, whereas expert and knowledge-oriented tasks s
The rapid advancement and deployment of multimodal large language models (MLLMs) necessitate deeper understanding of their internal mechanisms for responsible and reliable application.
A robust framework for analyzing modality interaction in MLLMs is crucial for identifying biases, improving performance, and building trustworthy AI systems in critical applications.
The introduction of Partial Information Decomposition (PID) offers a new analytical lens beyond traditional outcome-based evaluation, allowing for a more granular understanding of how different data modalities contribute to AI decisions.
- · AI researchers
- · MLLM developers
- · AI safety and ethics organizations
- · Developers of multimodal applications
- · Developers relying solely on black-box MLLMs
- · AI systems with poor modality integration
Improved interpretability and debugging of multimodal AI models becomes possible.
This enhanced understanding could lead to more robust and less biased MLLMs, accelerating their adoption in sensitive domains such as healthcare or defense.
The ability to precisely tailor modality contributions might enable more efficient model architectures and specialized MLLMs for diverse tasks, potentially reducing computational overhead.
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Read at arXiv cs.AI