UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models
Aug 1, 2025·,,,,
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Boyang Xue
Fei Mi
Qi Zhu
Hongru Wang
Rui Wang
Sheng Wang
Erxin Yu
Xuming Hu
Kam-Fai Wong
Abstract
UAlign leverages uncertainty estimations to improve factuality alignment in large language models, addressing the critical challenge of ensuring factual accuracy in LLM outputs.
Type
Publication
In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Sheng Wang
(Forence)
PhD Graduate in Computer Science
Sheng Wang is a PhD graduate from The University of Hong Kong, supervised by Prof. Chuan Wu and Prof. Lingpeng Kong.
His research focuses on Agent, LLM Super-Alignment, and Data Synthesis. He has published 14+ papers in top-tier
conferences including NIPS2025 (Spotlight), ICLR2025, ACL2024/2025, EMNLP2025.
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