Cover Image

Language Model Uncertainty Quantification with Attention Chain

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Yinghao, Qiang, Rushi, Moukheiber, Lama, Zhang, Chao
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports
by: Moukheiber, Lama, et al.
Published: (2025)

Unmasking Societal Biases in Respiratory Support for ICU Patients through Social Determinants of Health
by: Moukheiber, Mira, et al.
Published: (2025)

Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
by: Moukheiber, Dana, et al.
Published: (2024)

Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
by: Li, Yinghao, et al.
Published: (2023)

Uncertainty Quantification for Large Language Diffusion Models
by: Vazhentsev, Artem, et al.
Published: (2026)

BiLoRA: A Bi-level Optimization Framework for Overfitting-Resilient Low-Rank Adaptation of Large Pre-trained Models
by: Qiang, Rushi, et al.
Published: (2024)

Recurrent Confidence Chain: Temporal-Aware Uncertainty Quantification in Large Language Models
by: Mao, Zhenjiang, et al.
Published: (2026)

A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction
by: Li, Yinghao, et al.
Published: (2024)

Uncertainty-Aware Attention Heads: Efficient Unsupervised Uncertainty Quantification for LLMs
by: Vazhentsev, Artem, et al.
Published: (2025)

Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models
by: Chen, Zizhang, et al.
Published: (2024)

CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought
by: Zhang, Boxuan, et al.
Published: (2025)

Uncertainty Quantification for In-Context Learning of Large Language Models
by: Ling, Chen, et al.
Published: (2024)

SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
by: Gao, Xiang, et al.
Published: (2024)

Towards Better Instruction Following Retrieval Models
by: Zhuang, Yuchen, et al.
Published: (2025)

Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models
by: Duan, Jinhao, et al.
Published: (2023)

Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
by: Liu, Xiaoou, et al.
Published: (2025)

ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
by: Li, Mingda, et al.
Published: (2025)

Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models
by: Zhou, Kevin, et al.
Published: (2025)

Prompt-Dependent Ranking of Large Language Models with Uncertainty Quantification
by: Menendez, Angel Rodrigo Avelar, et al.
Published: (2026)

Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
by: Song, Haoyi, et al.
Published: (2025)

The Consistency Hypothesis in Uncertainty Quantification for Large Language Models
by: Xiao, Quan, et al.
Published: (2025)

Agentic Uncertainty Quantification
by: Zhang, Jiaxin, et al.
Published: (2026)

HYDRA: Model Factorization Framework for Black-Box LLM Personalization
by: Zhuang, Yuchen, et al.
Published: (2024)

COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation
by: Wang, Sean, et al.
Published: (2025)

Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions
by: Kang, Sungmin, et al.
Published: (2025)

Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses
by: Chen, Tiejin, et al.
Published: (2025)

Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
by: Lin, Zhen, et al.
Published: (2023)

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
by: Vashurin, Roman, et al.
Published: (2024)

Uncertainty Quantification in Large Language Models Through Convex Hull Analysis
by: Catak, Ferhat Ozgur, et al.
Published: (2024)

LUQ: Long-text Uncertainty Quantification for LLMs
by: Zhang, Caiqi, et al.
Published: (2024)

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models
by: Vazhentsev, Artem, et al.
Published: (2025)

SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models
by: Bhattacharjya, Debarun, et al.
Published: (2025)

Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
by: Li, Changhao, et al.
Published: (2024)

TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
by: Wang, Haorui, et al.
Published: (2024)

ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models
by: Heng, Yuzhao, et al.
Published: (2024)

URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models
by: Nguyen, Vinh, et al.
Published: (2026)

Uncertainty Quantification for LLM Function-Calling
by: Ye, Zihuiwen, et al.
Published: (2026)

Benchmarking LLMs via Uncertainty Quantification
by: Ye, Fanghua, et al.
Published: (2024)

AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning
by: Zhang, Ruiyi, et al.
Published: (2024)

UQLM: A Python Package for Uncertainty Quantification in Large Language Models
by: Bouchard, Dylan, et al.
Published: (2025)