arXiv AI June 10, 2026 By Johanne Medina, Tianyi Zhou, Keivin Isufaj, Aristides Gionis, Sanjay Chawla

Integrating Local and Global Entropy for Uncertainty Quantification in LLMs

arXiv:2606. 09875v1 Announce Type: cross Abstract: Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 5

LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics

arXiv:2603. 24929v2 Announce Type: replace Abstract: Understanding and quantifying uncertainty in large language model (LLM) outputs is critical for reliable deployment.

By Farhan Ahmed, Yuya Jeremy Ong, Chad DeLuca

llms safety

arXiv Computation and Language

Aug 25

Kernel Token Contradiction: a Fast and Principled Approach for LLM Claim Uncertainty Quantification

arXiv:2608.22506v1 Announce Type: new Abstract: Claim-level Uncertainty Quantification (UQ) aims to mitigate the lack of reliability of Large Language Models (LLMs) by evaluating the factuality of ea...

By J\'er\'emie Dentan, Alexi Canesse, Mahammed El Sharkawy, Sonia Vanier

llms benchmarks

arXiv AI

Jul 28

TRE: Training-Free Hallucination Detection for Diffusion Language Models

arXiv:2607. 22661v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have recently gained increasing attention, yet their reliability is significantly hindered by the hallucination problem.

By Pengcheng Weng, Yanyu Qian, Yue Tan, Yixin Liu

llms diffusion safety

arXiv Machine Learning

Jul 14

Reliability Scaling Laws for Quantized Large Language Models

arXiv:2607. 10855v1 Announce Type: new Abstract: Quantization is a powerful strategy to build capable and resource-efficient large language models (LLMs) by reducing the bitwidth of the parameters.

By Sirine Ayadi, S\'andor Dar\'oczi, Stephan G\"unnemann, Bertrand Charpentier

llms efficiency benchmarks

arXiv AI

Aug 10

INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimation

arXiv:2603. 21607v2 Announce Type: replace Abstract: While retrieval-augmented generation (RAG) significantly improves the factual reliability of LLMs, it does not eliminate hallucinations, so robust uncertainty quantification (UQ) remains essential.

By Alexandra Bazarova, Andrei Volodichev, Daria Kotova, Alexey Zaytsev

llms rag benchmarks safety

arXiv AI

Aug 26

Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review

arXiv:2504.18346v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been transformative across many domains. However, hallucination, i.e., confidently outputting incorrect inf...

By Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang, Leon Witt, Muhammad Asif Ali, Yukai Miao, Dan Li, Qingsong Wei

llms benchmarks safety

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LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics

Kernel Token Contradiction: a Fast and Principled Approach for LLM Claim Uncertainty Quantification

TRE: Training-Free Hallucination Detection for Diffusion Language Models

Reliability Scaling Laws for Quantized Large Language Models

INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimation

Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review