Unverified50% confidenceFactExact time
The uncertainty quantification (UQ) mechanism is used to assess the confidence of prediction results, ensuring that the validity of the surrogate model's outputs is judged within a user-specified tolerance.
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10/30/2026
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UnverifiedGaussian output distribution is the most fundamental form of predictive uncertainty quantification (UQ), where the model outputs distribution parameters (mean and variance) rather than a single scalar.72% similarUnverifiedHaving models explicitly output confidence or evidence-strength labels, introducing uncertainty-aware loss functions, and attaching verifiable source spans to statements through retrieval-augmented generation (RAG) are related research directions for handling uncertainty.70% similarUnverifiedThe system fuses the predicted probabilities of the two validators to produce the final factor determination result.70% similarUnverifiedThe value of enterprise-grade models involves multi-dimensional metrics including accuracy, inference cost, response latency, controllability, and safety.69% similarVerifiedThe RLHF (Reinforcement Learning from Human Feedback) training method optimizes for the model's overconfidence deficiency, training the model to proactively express uncertainty when uncertain.68% similar
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