Preprint2024
Benchmarking llms via uncertainty quantification
Unknown
Introduces a new benchmarking approach for LLMs that integrates uncertainty quantification, evaluated across nine LLMs spanning five series.
0Jan 1, 2024Benchmarks
AI Research
AI & ML Research Papers
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
All Papers
11 papersPreprint2018
Neural network-based uncertainty quantification: A survey of methodologies and applications
Unknown
This survey provides a comprehensive overview of neural network-based uncertainty quantification methodologies and their applications in decision making.
0Jan 1, 2018Neural Networks
Preprint2026
A survey on uncertainty quantification methods for deep learning
Unknown
This survey categorizes uncertainty quantification methods for deep learning and demonstrates their application across various domains.
0Jan 1, 2026
Preprint2023
Enhancing uncertainty-based hallucination detection with stronger focus
Unknown
This paper enhances uncertainty-based hallucination detection in LLMs by focusing on sentence-level detection, improving performance on accessible datasets.
0Jan 1, 2023
Preprint2023
Do large language models know what they don't know?
Unknown
This paper investigates whether large language models can accurately assess their own knowledge and uncertainty, proposing methods to improve self-awareness and calibration.
0Jan 1, 2023Large Language Models
Preprint2026
Reasoning about uncertainty: Do reasoning models know when they don't know?
Unknown
This paper evaluates uncertainty quantification methods for reasoning models without fine-tuning, using self-verbalized approaches to assess when models know they don't know.
0Jan 1, 2026Reasoning
JournalNature2024
Detecting hallucinations in large language models using semantic entropy
Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, et al.
Proposes semantic entropy to detect LLM confabulations by measuring uncertainty at the meaning level, enabling task-agnostic hallucination detection.
1.5kJun 19, 2024Large Language ModelsPrompting
PreprintArtificial Intelligence Review2023
A survey of uncertainty in deep neural networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, et al.
A comprehensive survey of uncertainty estimation in deep neural networks, covering sources, modeling approaches, calibration, and practical applications.
1.9kJul 29, 2023Ai SafetyNeural Networks
arXiv
PreprintJournal of Artificial Intelligence Research1999
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
Craig Boutilier, Taraneh Dean, Steve Hanks
This paper surveys how structural properties of MDPs can be exploited via AI-style representations to ease the computational burden of planning under uncertainty.
1.3kJul 1, 1999
arXiv
PreprintarXiv.org2026
When to Retrieve During Reasoning
Dong-Sheng Guo, Jikun Wu, S. Yiu
ReaLM-Retrieve introduces a reasoning-aware retrieval framework that injects external evidence at step-level uncertainty during multi-step reasoning, improving F1 by 10.1% over standard RAG while reducing retrieval calls by 47%.
0Apr 29, 2026Retrieval Augmented GenerationReasoning
arXiv
PreprintInformation Fusion2021
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, et al.
Comprehensive review of uncertainty quantification methods in deep learning, covering Bayesian and ensemble techniques across vision, NLP, and reinforcement learning.
2.7kMay 23, 2021Reinforcement LearningComputer Vision
arXiv