Workshop on "Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI"

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Overview

How can we trust large language models (LLMs) when they generate text with confidence, but sometimes hallucinate or fail to recognize their own limitations? As foundation models like LLMs and multimodal systems become pervasive across high-stakes domains—from healthcare and law to autonomous systems—the need for uncertainty quantification (UQ) is more critical than ever. Uncertainty quantification provides a measure of how much confidence a model has in its predictions, allowing users to assess when to trust the outputs and when human oversight may be needed.