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AI Research
arXiv CS.AI·Jun 12·10 min read
Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models
TL;DR
Multimodal attention-based models have advanced significantly, but their decision-making processes remain largely opaque. A systematic review of literature from 2020 to 2024 was conducted to assess explainability techniques in these models.
✦ Why It Matters
Engineers can enhance their multimodal AI systems by adopting standardized evaluation practices for explainability.
Key Takeaways
- Focus on vision-language and language-only models for explainability research.
- Utilize attention techniques to enhance model explanations.
- Implement standardized evaluation methods for multimodal XAI.
- Address the interactions between different data modalities in model design.
How It Works
Attention-based models prioritize certain parts of the input data, allowing them to focus on relevant features when making predictions. This mechanism is crucial for interpreting how these models arrive at their decisions, especially in multimodal contexts where different data types interact.