MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
Aug 1, 2025·,,
,,,,,·
0 min read
Boyang Xue
Hongru Wang
Rui Wang
Sheng Wang
Zezhong Wang
Yiming Du
Bin Liang
Wenxuan Zhang
Kam-Fai Wong
Abstract
MlingConf presents a comprehensive study of multilingual confidence estimation on large language models, exploring how confidence calibration varies across different languages and providing insights for improving multilingual LLM reliability.
Type
Publication
In Findings of the Association for Computational Linguistics: ACL 2025
Authors
Authors
Authors
Authors
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.
Authors
Authors
Authors
Authors
Authors