Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis
arXiv:2606.07533v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues. However, the internal mechanisms by which heterogeneous modalities influence model behavior remain opaque. While Shapley Values (SV) provide a robust, model-agnostic framework for local explainability in text-based NLP, their extension to multimodal data is hindered by cross-channel dependencies, intricate dialogue structures, and the prohibitive computational complexity of dense audio representations. In this wor
The rapid advancement and integration of Multimodal Large Language Models (MLLMs) necessitate a deeper understanding of their internal workings to ensure reliability and trust.
Improved explainability for MLLMs is crucial for their adoption in high-stakes applications, fostering trust, and enabling targeted improvements, particularly as autonomous AI systems become more prevalent.
This research provides a framework for applying traditional explainability methods to complex multimodal AI, potentially accelerating development and deployment by making these models more auditable.
- · AI researchers and developers
- · Companies deploying MLLMs in critical applications
- · Regulatory bodies and auditors
- · Users of AI systems
- · Companies with opaque AI systems
- · Anyone relying on unexplainable black-box models
Enhanced XAI for MLLMs leads to more robust and trustworthy AI applications.
Increased trust and understanding could accelerate the deployment of autonomous AI agents across various sectors.
The ability to audit and explain complex AI decisions could become a mandatory requirement for widespread AI integration, leading to new industry standards and regulatory frameworks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI