MLLM-Shap: A Shapley Value Approach to Inject Interpretability into Multimodal Large Language Models

The Data Science Undergraduate Program at Warsaw University of Technology proposes the MLLM-Shap method, which introduces the concept of Shapley values from game theory into the field of multimodal large language models (MLLMs). It aims to solve the black-box problem of MLLMs and provide an interpretability analysis tool. This method focuses on feature attribution, addresses challenges in multimodal scenarios, and helps with model debugging, bias detection, and user trust building. It is an innovative attempt to combine classical XAI theory with cutting-edge models.

Multimodal large language models (such as GPT-4V and Gemini) have cross-modal reasoning capabilities, but their decision-making processes are opaque, leading to difficulties in trust and limited deployment in high-risk scenarios. The field of Explainable Artificial Intelligence (XAI) is seeking solutions, and the MLLM-Shap project is an attempt to introduce Shapley values into MLLM interpretability research.

Shapley values originate from game theory. When transferred to machine learning, predictions are treated as total gains, input features as participants, and the marginal contribution of each feature is calculated to measure its importance. Its advantages include: 1. Axiomatic theoretical foundation (satisfies axioms such as efficiency and symmetry); 2. Naturally considers feature interactions (adapts to multimodal collaboration); 3. Model agnosticism (applicable to complex MLLMs).

Applying Shapley values to MLLMs requires addressing three major challenges: 1. Modal heterogeneity (differences between discrete text symbols and continuous image pixels); 2. High-dimensional input space (needs efficient approximation algorithms); 3. Complexity of generative outputs (difficulty in assigning importance to generated tokens).

The technical implementation of MLLM-Shap includes: 1. Feature granularity selection (hierarchical strategy, supporting token/phrase-level text and pixel/patch-level images); 2. Approximation algorithm optimization (KernelSHAP reduces model calls, GradientSHAP uses gradients for acceleration); 3. Multimodal attribution visualization (text heatmaps, image attention/saliency maps).

The application value of MLLM-Shap is reflected in: 1. Model debugging (locating the root cause of errors); 2. Bias detection (identifying over-reliance on irrelevant features); 3. User trust building (transparently showing decision-making basis, enhancing human-machine interaction).

MLLM-Shap has limitations: 1. Computational efficiency bottleneck (needs architecture-specific acceleration algorithms); 2. Difficulty in evaluating explanation quality (lack of standardized metrics); 3. Lack of causal relationships (needs to integrate causal inference techniques). Future optimizations will target these directions.

MLLM-Shap bridges academic exploration and practical applications, emphasizing that interpretability should be the core of model design. As multimodal AI is applied in key scenarios, such tools will become essential components of responsible AI development, providing developers and researchers with a starting point to understand MLLM behavior.