Unverified50% confidenceFactExact time
Having 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.
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50%
Confidence
Medium-term (~90 days)
Relevance
10/23/2026
First Seen
Valid until: 1/21/2027
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Related Claims
VerifiedSURE uncertainty modeling enables the model to express confidence and abstain when evidence is insufficient, distinguishing between epistemic uncertainty and aleatoric uncertainty.74% similarUnverifiedAdding schema validation at the tool-calling stage constrains the uncertainty of model outputs to a controllable range.74% similarUnverifiedResearch directions for improving model calibration include introducing uncertainty tokens through supervised fine-tuning, performing post-calibration on probability distributions using temperature sampling, and training models to proactively output the ability to refuse to answer.73% similarUnverifiedDeveloping fact-checking mechanisms and uncertainty quantification methods can enable models to recognize and acknowledge the boundaries of their knowledge.72% similar
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