As we continue to push the boundaries of Explainable AI, I'd like to pose a question to our community: Can we design RAG systems that not only provide clear justifications for model predictions but also adapt to the uncertainty of real-world data, allowing them to dynamically update their explanations as new information becomes available? In other words, can RAG systems learn to learn and explain in an iterative, real-time loop?
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