Cover Image

Uncertainty Quantification of Large Language Models using Approximate Bayesian Computation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sharma, Mridul, Patel, Adeetya, Souza, Zaneta D', Rahimi, Samira Abbasgholizadeh, Reddy, Siva, Madathil, Sreenath
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Humans and LLMs Diverge on Probabilistic Inferences
by: Kamath, Gaurav, et al.
Published: (2026)

Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
by: Rodemann, Julian, et al.
Published: (2026)

Scope Ambiguities in Large Language Models
by: Kamath, Gaurav, et al.
Published: (2024)

Are self-explanations from Large Language Models faithful?
by: Madsen, Andreas, et al.
Published: (2024)

Language Models Largely Exhibit Human-like Constituent Ordering Preferences
by: Tur, Ada Defne, et al.
Published: (2025)

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

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

Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values
by: Lee, Seongjun, et al.
Published: (2026)

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

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

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

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

AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study
by: Rahimi, Iman, et al.
Published: (2025)

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

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)

From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models
by: Zhang, Jiaxin, et al.
Published: (2026)

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
by: BehnamGhader, Parishad, et al.
Published: (2024)

ClaudesLens: Uncertainty Quantification in Computer Vision Models
by: Shaar, Mohamad Al, et al.
Published: (2024)

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

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

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

MedBayes-Lite: Bayesian Uncertainty Quantification for Safe Clinical Decision Support
by: Hossain, Elias, et al.
Published: (2025)

Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining
by: Zhou, Xiaofan, et al.
Published: (2025)

Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space
by: Qiu, Xin, et al.
Published: (2024)

Reverse Probing: Supervised Token-level Uncertainty Quantification for Large Language Models in Clinical Text
by: Xiao, Bushi, et al.
Published: (2026)

BELL: Benchmarking the Explainability of Large Language Models
by: Ahmed, Syed Quiser, et al.
Published: (2025)

POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
by: Menet, Nicolas, et al.
Published: (2026)

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)

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions
by: Shorinwa, Ola, et al.
Published: (2024)

SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory
by: Zhao, Xingtao, et al.
Published: (2025)

Large Language Models as Computable Approximations to Solomonoff Induction
by: Wan, Jun, et al.
Published: (2025)

Deep Learning Decision Support System for Open-Pit Mining Optimisation: GPU-Accelerated Planning Under Geological Uncertainty
by: Rahimi, Iman
Published: (2025)

GNN's Uncertainty Quantification using Self-Distillation
by: Daneshvar, Hirad, et al.
Published: (2025)

Can Large Reasoning Models do Analogical Reasoning under Perceptual Uncertainty?
by: Camposampiero, Giacomo, 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)

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

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
by: Huang, Chengpiao, et al.
Published: (2025)

Robust Uncertainty Quantification for Factual Generation of Large Language Models
by: Zhang, Yuhao, et al.
Published: (2026)