Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis

📰 ArXiv cs.AI

Learn how to apply traditional explainability methods to multimodal multilingual models using XAI-based analysis to improve model transparency and interpretability

advanced Published 9 Jun 2026

Action Steps

Apply Shapley Values (SV) to text-based NLP models to establish a baseline for explainability
Extend SV to multimodal data by accounting for cross-channel dependencies and intricate dialogue structures
Use XAI-based analysis to identify key factors influencing model behavior in multimodal settings
Evaluate the effectiveness of XAI-based analysis in improving model transparency and interpretability
Integrate XAI-based analysis into the model development pipeline to ensure ongoing explainability and trustworthiness

Who Needs to Know This

Data scientists and AI engineers working on multimodal multilingual models can benefit from this analysis to improve model explainability and trustworthiness. This can also inform product managers and designers on how to design more transparent and interpretable AI systems

Key Insight

💡 XAI-based analysis can bridge the gap between traditional explainability methods and multimodal multilingual models, enabling more transparent and trustworthy AI systems