mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models
arXiv:2606.07531v1 Announce Type: cross Abstract: We introduce mllm-shap, an open-source Python framework designed to extend Shapley Value (SV) explainability from text-only Large Language Models to Multimodal LLMs (MLLMs) processing joint text and audio inputs. While text-based attribution is well-studied, mllm-shap addresses three critical challenges unique to the multimodal regime: (1) Modality-aware coalition masking, which manages the interleaved processing of discrete text tokens and dense audio encoder frames. (2) Multi-turn conversation tracking, utilizing per-token metadata to maintai
As multimodal LLMs become more prevalent and complex, the need for transparent explainability tools is emerging to ensure trust and reliability in their advanced applications.
Understanding how multimodal LLMs make decisions is crucial for their adoption in high-stakes environments, addressing ethical concerns, and accelerating their development.
The development of specific tools like mllm-shap shifts explainability from a research problem for text-only models to a practical, implementable feature for multimodal AI systems.
- · AI developers
- · Auditors and regulators
- · Industries deploying MLLMs
- · Black-box MLLM vendors
- · Proprietary-only explainability solutions
Increased understanding and debugging capabilities for complex multimodal AI models.
Faster development and deployment of MLLMs in sensitive sectors due to improved trust and compliance.
Standardization of explainability techniques across different multimodal AI platforms, potentially leading to new regulatory frameworks for AI transparency.
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Read at arXiv cs.AI