Work

Survey of uncertainty estimation in LLMs - Sources, methods, applications, and challenges

Year: 2025

Type: article

Source: Information Fusion

DOI: 10.1016/j.inffus.2025.104057

Authors: Jianfeng He, Linlin Yu, Changbin Li, Runing Yang, Fanglan Chen, Kangshuo Li, Min Zhang, Shuo Lei, Xuchao Zhang, Mohammad Beigi, Kaize Ding, Bei Xiao, Lifu Huang, Feng Chen, Ming Jin, Chang-Tien Lu

Institutions: Virginia Tech, Augusta University, The University of Texas at Dallas, Microsoft (United States), University of California, Davis, Northwestern University, American University

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funder: National Science Foundation

Awards: Collaborative Research: SLES: Safety under Distributional Shift in Learning-Enabled Power Systems, Embodied Optimization for Decision-Making in Dynamic and Uncertain Environments, ATD: Sparse and Localized Graph Convolutional Networks for Anomaly Detection and Active Learning, FAI: A novel paradigm for fairness-aware deep learning models on data streams, III: Medium: Towards Inclusive Recommendation Systems with Stakeholder Alignment, III: Medium: Collaborative Research: MUDL: Multidimensional Uncertainty-Aware Deep Learning Framework

OpenAlex ID: W4417506933

FWCI: 1.709

Cites: 19

Cited by: 4

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