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technologyreview.com·3h ago
AI Research
arXiv CS.AI·Jun 9·6 min read
Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis
TL;DR
Multimodal Large Language Models (MLLMs) struggle with explainability due to complex interactions between text and audio inputs. A new framework extends Shapley Values (SV) to analyze these models by treating text and audio as cooperative features, using efficient estimation strategies.
✦ Why It Matters
Engineers can leverage this framework to enhance explainability in multimodal AI applications, improving model transparency.
Key Takeaways
- Utilize the open-source Python package for multimodal explainability.
- Apply efficient estimation strategies for Shapley Values in your models.
- Leverage SGPA for better audio-text alignment in your datasets.
- Evaluate model attributions across different input modalities.
How It Works
The proposed framework extends Shapley Values to multimodal contexts by treating text and audio as cooperative features. It employs efficient estimation techniques to compute attributions without excessive computational costs.
The SGPA method aligns audio segments with text tokens, allowing for clearer interpretation of how different modalities influence model behavior.