Knowledge Tree
Uncertainty quantification
Facts (29)
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A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 15 facts
perspectiveThe authors of 'A Comprehensive Review of Neuro-symbolic AI for Robustness' identify three foundational factors essential for establishing neuro-symbolic AI as a trustworthy paradigm: robustness, uncertainty quantification (UQ), and intervenability.
claimNeuro-symbolic AI offers a promising alternative to conventional deep learning frameworks for addressing challenges related to model robustness, uncertainty quantification, and human intervenability.
referenceLarge Language Models and multimodal systems introduce unique uncertainty challenges, such as uncertainty compounding during autoregressive generation and dynamic shifts in uncertainty based on context, requiring uncertainty quantification approaches tailored to their specific characteristics rather than methods for simpler discriminative models, as cited in reference [56].
referenceThe paper 'Uncertainty quantification for neurosymbolic programs via compositional conformal prediction' was authored by Ramalingam, R., Park, S., and Bastani, O., and published as an arXiv preprint (arXiv:2405.15912) in 2024.
claimIn the context of machine learning, uncertainty quantification (UQ) refers to the process of providing a measure of how much confidence a model has in its predictions or generations.
referenceHuang, Lam, and Zhang developed methods for efficient uncertainty quantification and reduction for over-parameterized neural networks in their 2023 paper.
claimK. Acharya and H. Song authored the article 'A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and Intervenability', which was published in the Arab Journal of Science and Engineering, volume 51, pages 35–67, in 2026.
claimThe article 'A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and Intervenability' is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, provided appropriate credit is given to the original authors and source.
claimUncertainty Quantification (UQ) is essential in domains such as robotic sensing in noisy environments, medical AI diagnosis with incomplete information, and autonomous drone navigation with partial observability.
claimUncertainty Quantification is embedded in neuro-symbolic models through methods such as probabilistic symbolic reasoning, Bayesian neural modules, or fuzzy logic systems.
referenceR.C. Smith authored the book 'Uncertainty Quantification: Theory, Implementation, and Applications,' which was published by SIAM in 2024.
referenceWenzel et al. explored the use of hyperparameter ensembles for robustness and uncertainty quantification in their 2020 paper.
claimThe research article 'A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and Intervenability' was partially supported by the U.S. National Science Foundation through Grant No. 2317117.
claimRobustness in AI models is defined as the ability to maintain performance under varied and unforeseen conditions, while Uncertainty Quantification (UQ) provides a measure of confidence in model predictions, and intervenability enables human operators to effectively intervene in AI system operations.
claimRobustness, uncertainty quantification (UQ), and intervenability are identified as the three interdependent pillars crucial for enhancing the trustworthiness of AI-driven decision-making.
Track: Poster Session 3 - aistats 2026 virtual.aistats.org 5 facts
perspectiveJames McInerney and Nathan Kallus argue that uncertainty quantification in deep learning is crucial for safe and reliable decision-making in downstream tasks.
claimZijun Gao proposes incorporating uncertainty quantification into HTE estimator comparisons and shifting the focus to the estimation and inference of the relative error between methods rather than absolute errors.
claimZheyang Shen, Jeremias Knoblauch, Sam Power, and Chris Oates propose 'Prediction-Centric Uncertainty Quantification,' a method where a mixture distribution based on a deterministic model provides improved uncertainty quantification in predictive contexts, addressing the issue where misspecified deterministic models lead to incorrect, overly certain posterior predictions.
claimThe authors of the paper on Strategic Conformal Prediction propose a framework designed for robust uncertainty quantification in settings where machine learning model predictions alter the environment because agents strategize to suit their own interests.
claimThe causal inference estimator proposed by the authors achieves semiparametric efficiency under mild regularity conditions, which enables consistent uncertainty quantification.
Re-evaluating Hallucination Detection in LLMs - arXiv arxiv.org Aug 13, 2025 3 facts
referenceLin et al. (2023) proposed a method for uncertainty quantification in black-box Large Language Models titled 'Generating with Confidence'.
referenceNikitin et al. (2024) introduced 'Kernel language entropy', a method for fine-grained uncertainty quantification for Large Language Models based on semantic similarities.
referenceXin Qiu and Risto Miikkulainen proposed 'Semantic density' in 2024 as a method for uncertainty quantification in large language models by measuring confidence in semantic space.
Medical Hallucination in Foundation Models and Their ... medrxiv.org Mar 3, 2025 3 facts
procedureUncertainty Quantification uses sequence log-probability and semantic entropy measures to identify potential areas of Clinical Data Fabrication and Procedure Description Errors in Large Language Models.
claimAdvanced Large Language Model frameworks, such as the embodied agent Voyager, often lack robust uncertainty quantification, even outside the medical domain, as observed by Wang et al. (2023).
claimDetection and mitigation strategies for medical hallucinations in Foundation Models include factual verification, consistency checks, uncertainty quantification, and prompt engineering.
LLM Hallucination Detection and Mitigation: State of the Art in 2026 zylos.ai Jan 27, 2026 2 facts
procedureUncertainty quantification in LLMs is primarily approached through three methods: logit-based methods (analyzing internal probability distributions), sampling-based methods (assessing variability across multiple generations), and verbalized confidence (prompting the model to express its own confidence).
referenceThe paper 'Uncertainty Quantification in LLMs: A Survey,' published by ACM, provides a comprehensive overview of methods for quantifying uncertainty in large language models.
Awesome-Hallucination-Detection-and-Mitigation - GitHub github.com 1 fact
referenceBouchard et al. (2025) published 'Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers' on Arxiv.