concept

uncertainty estimation

Also known as: uncertainty estimates, uncertainty estimations

Facts (19)

Sources

Medical Hallucination in Foundation Models and Their ... medrxiv.org medRxiv Mar 3, 2025 5 facts

claimIn clinical settings, Large Language Models (LLMs) require robust mechanisms for uncertainty estimation because inaccurate or ungrounded outputs can mislead decision-making.

claimUncertainty estimation strategies, including post-hoc calibration, structured confidence sets, and consensus-driven deliberation, allow practitioners to better interpret and validate AI outputs in healthcare by effectively conveying when models are uncertain.

claimYuan et al. (2023) highlight the need for improved uncertainty estimation techniques to mitigate overconfidence in large language models.

claimEncouraging large language models to output uncertainty estimates or alternative explanations can address overconfidence and premature closure biases, particularly when users are guided to critically evaluate multiple options.

procedureEffective strategies for addressing poor calibration in large language models include probabilistic modeling, confidence-aware training, and ensemble methods, which enable models to provide uncertainty estimates alongside predictions.

LLM Hallucinations: Causes, Consequences, Prevention - LLMs llmmodels.org llmmodels.org May 10, 2024 5 facts

claimStrategies to mitigate hallucinations in large language models include using high-quality training data, employing contrastive learning, implementing human oversight, and utilizing uncertainty estimation.

claimOngoing research areas to address LLM hallucinations include contrastive learning, knowledge grounding, consistency modeling, and uncertainty estimation.

procedureUncertainty estimation as a mitigation strategy for large language model hallucinations involves enabling the models to recognize when they lack sufficient information.

claimOngoing research to address LLM hallucinations includes techniques such as contrastive learning, knowledge grounding, consistency modeling, and uncertainty estimation.

claimUncertainty estimation is an approach to mitigate LLM hallucinations by enabling large language models to recognize when they are uncertain or lack sufficient information.

LLM Hallucination Detection and Mitigation: State of the Art in 2026 zylos.ai Zylos Jan 27, 2026 4 facts

measurementMulti-layered approaches that combine retrieval-augmented generation (RAG), uncertainty estimation, self-consistency methods, and guardrails achieve a 40-96% reduction in hallucinations.

procedureProduction deployment of LLMs requires stacking multiple techniques to mitigate hallucinations, specifically: RAG for knowledge grounding, uncertainty estimation for confidence scoring, self-consistency checking for validation, and real-time guardrails for critical applications.

measurementModern hallucination mitigation approaches, which combine uncertainty estimation, self-consistency checking, retrieval augmentation, and real-time guardrails, can reduce hallucination rates by up to 96% in production systems.

claimThe NVIDIA NeMo and Cleanlab Trustworthy Language Model (TLM) integration provides state-of-the-art uncertainty estimation, trustworthiness scoring for each response, and integrated safety checks.

Re-evaluating Hallucination Detection in LLMs - arXiv arxiv.org arXiv Aug 13, 2025 2 facts

referenceThe paper 'LM-polygraph: Uncertainty estimation for language models' by Fadeeva et al. (2023) presents a framework for uncertainty estimation in language models, published in the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

referenceMalinin and Gales (2021) researched uncertainty estimation techniques specifically for autoregressive structured prediction.

Awesome-Hallucination-Detection-and-Mitigation - GitHub github.com GitHub 2 facts

referenceKuhn et al. (2023) published 'Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation' in ICLR, focusing on uncertainty estimation.

referenceThe paper "UALIGN: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models" by Xue et al. (2025) introduces a method for aligning large language models with factuality using uncertainty estimations.

A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer Dec 9, 2025 1 fact

referenceLaurent et al. introduced 'Packed-ensembles' as a method for efficient uncertainty estimation in their 2022 arXiv preprint.