Hugging Face Trending Papers July 7, 2026

SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation

Read the original on Hugging Face Trending Papers →

Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement in LLM generation. However, existing approaches operate at suboptimal granularities: token-level scores lack semantic coherence, while sequence-level scores fail to localize errors.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

Hugging Face Trending Papers

Jun 22

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models

Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for ensuring predictive credibility. While traditional uncertainty taxonomy paradigms, such as the dichotomy of aleatoric and epistemic uncertainties, provide conceptual foundations, they often fail to capture the multi-component and multi-stage nature of LLM generation and struggle to evaluate the effectiveness of various Uncertainty Quantification (UQ) methods.

llms benchmarks

arXiv AI

Jun 19

A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models

arXiv:2606. 19868v1 Announce Type: new Abstract: Although large language models (LLMs) have shown strong capabilities across a wide range of tasks, their outputs often remain unreliable and may contain hallucinations, making uncertainty estimation (UE) essential for building trustworthy LLMs.

By Jiayi Wang, Xu-Yao Zhang

llms agents benchmarks safety

arXiv AI

Jul 7

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language

arXiv:2607. 03882v1 Announce Type: cross Abstract: LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information in natural language.

By Diego Cerda-Mardini, Sarath Chandar, Sreenath Madathil

llms

arXiv AI

Jun 3

SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory

arXiv:2511. 16275v4 Announce Type: replace-cross Abstract: Reliable uncertainty quantification (UQ) is essential for deploying large language models (LLMs) in safety-critical scenarios, as it enables them to abstain from responding when uncertain, thereby avoiding hallucinations, i.

By Xingtao Zhao, Hao Peng, Dingli Su, Xianghua Zeng, Chunyang Liu, Jinzhi Liao, Philip S. Yu

llms safety

arXiv AI

Jun 2

Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models

arXiv:2605. 04638v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate.

By Mingda Li, Rundong Lv, Xinyu Li, Weinan Zhang, Ting Liu

llms rag benchmarks

arXiv AI

Jul 8

Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs

arXiv:2607. 06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English.

By Andrea Alfarano, Andrea Bacciu, Saab Mansour, Amin Mantrach, Marcello Federico

llms rag

Related stories

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models

A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language

SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory

Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models

Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs