Cover Image

On Subjective Uncertainty Quantification and Calibration in Natural Language Generation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Ziyu, Holmes, Chris
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Skewed Memorization in Large Language Models: Quantification and Decomposition
by: Li, Hao, et al.
Published: (2025)

IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation
by: Fan, Haozhi, et al.
Published: (2026)

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

Adaptive Uncertainty Quantification for Generative AI
by: Kim, Jungeum, et al.
Published: (2024)

Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs
by: Sahoo, Subramanyam
Published: (2026)

On Uncertainty In Natural Language Processing
by: Ulmer, Dennis
Published: (2024)

Multi-group Uncertainty Quantification for Long-form Text Generation
by: Liu, Terrance, et al.
Published: (2024)

Uncertainty in Language Models: Assessment through Rank-Calibration
by: Huang, Xinmeng, et al.
Published: (2024)

Revisiting Uncertainty Estimation and Calibration of Large Language Models
by: Tao, Linwei, et al.
Published: (2025)

Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings
by: Grewal, Yashvir S., et al.
Published: (2024)

Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models
by: Cao, Qi, et al.
Published: (2026)

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

Natural Language Reinforcement Learning
by: Feng, Xidong, et al.
Published: (2024)

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

Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models
by: Pan, Shuaidong, et al.
Published: (2025)

Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification
by: Bouchard, Dylan, et al.
Published: (2026)

Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
by: Nikitin, Alexander, et al.
Published: (2024)

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
by: Fadeeva, Ekaterina, et al.
Published: (2024)

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

Natural Language Reinforcement Learning
by: Feng, Xidong, et al.
Published: (2024)

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
by: Krishnan, Ranganath, et al.
Published: (2024)

Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning
by: Tsai, Yao-Hung Hubert, et al.
Published: (2024)

Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study
by: Bouchard, Dylan, et al.
Published: (2026)

Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results
by: Santilli, Andrea, et al.
Published: (2025)

An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
by: Grünefeld, Nils, et al.
Published: (2026)

The Role of Ambiguity in Error Prediction via Uncertainty Quantification
by: Staliūnaitė, Ieva Raminta, et al.
Published: (2026)

Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers
by: Bouchard, Dylan, et al.
Published: (2025)

Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
by: Kirchhof, Michael, et al.
Published: (2025)

Calibrating Large Language Models Using Their Generations Only
by: Ulmer, Dennis, et al.
Published: (2024)

Optimizing Decoding Paths in Masked Diffusion Models by Quantifying Uncertainty
by: Chen, Ziyu, et al.
Published: (2025)

Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks
by: Hashimoto, Wataru, et al.
Published: (2024)

Calibrated Physics-Informed Uncertainty Quantification
by: Gopakumar, Vignesh, et al.
Published: (2025)

Uncertainty Quantification for Transformer Models for Dark-Pattern Detection
by: Muñoz, Javier, et al.
Published: (2024)

Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
by: Kruse, Maya, et al.
Published: (2025)

Calibrating Long-form Generations from Large Language Models
by: Huang, Yukun, et al.
Published: (2024)

An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
by: Pavlovic, Maja, et al.
Published: (2026)

On the Entropy Calibration of Language Models
by: Cao, Steven, et al.
Published: (2025)

Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting
by: Dai, Hui, et al.
Published: (2026)

Multi-Agent Reasoning with Consistency Verification Improves Uncertainty Calibration in Medical MCQA
by: Martinez, John Ray B.
Published: (2026)

Planning In Natural Language Improves LLM Search For Code Generation
by: Wang, Evan, et al.
Published: (2024)