Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition
Researchers have developed a new framework called Partial Information Decomposition (PID) to analyze how different modalities interact within multimodal large language models (MLLMs). PID quantifies the unique, redundant, and synergistic contributions of various inputs, offering insights beyond traditional evaluation methods. The framework reveals that tasks requiring reasoning and grounding benefit most from synergistic modality interaction, while knowledge-intensive tasks rely more heavily on language alone. This approach can also predict model sensitivity to modality changes and has shown promise in improving multimodal reasoning and grounding performance. AI
IMPACT Provides a novel method for understanding and potentially improving the integration of multiple data types in AI models.