Traceable and Explainable Multimodal Large Language Models: An Information-Theoretic View

Zihan Huang, Junda Wu, Rohan Surana, Raghav Jain, Tong Yu, Raghavendra Addanki, David Arbour, Sungchul Kim, Julian McAuley

Keywords: multimodal LLM, information theory

TL;DR: We introduce an information-theoretic framework that uses mutual information, a Concept Bottleneck, and an InfoNCE mechanism to explain how multimodal models align and integrate visual and textual inputs.

Abstract:

Existing multimodal large language models (MLLMs) often lack traceable and explainable mechanisms for visual-textual alignment, making it challenging to understand how textual instructions shape multimodal representations. To address this shortcoming, we propose an information-theoretic framework that clarifies how MLLMs handle and transform both text and visual inputs. In particular, we measure the visual information gain that arises from textual instructions and multimodal encodings, thereby illuminating how different modalities interact and contribute to the model’s overall processing.
Our framework decomposes the multimodal encoding process into layer-wise mutual information measures for better explainability, quantifying the visual contribution as the difference between unconditional and text-conditional mutual information. Specifically, inspired by the Information Bottleneck framework, we introduce a Concept Bottleneck that maps high-dimensional multimodal representations into an interpretable space, enabling tractable variational upper bounds on the mutual information between visual inputs and the model’s internal states. Furthermore, we quantify the contextual contribution introduced by textual cues via an InfoNCE mechanism that contrasts multimodal representations computed with and without text guidance. This dual perspective, facilitated by tractable variational upper bounds, provides insight into how visual information is encoded and filtered by textual instructions, while also highlighting the contextual information induced and enhanced by MLLMs.
Empirical findings demonstrate underexplored dynamics of visual-textual interaction within MLLMs,
underscoring how textual instructions distinctly shape visual representations and demonstrating how visual prompts,
when effectively paired with instructions, enhance multimodal understanding.

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Submission Number: 1884