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cs.AI2026
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
Ponhvoan Srey, Quang Minh Nguyen, Xiaobao Wu +1
Uncertainty estimation (UE) aims to detect hallucinated outputs of large language models (LLMs) to improve their reliability. However, UE metrics often exhibit unstable performance…
cs.AI2025
Affective-ROPTester: Capability and Bias Analysis of LLMs in Predicting Retinopathy of Prematurity
Shuai Zhao, Yulin Zhang, Luwei Xiao +7
Despite the remarkable progress of large language models (LLMs) across various domains, their capacity to predict retinopathy of prematurity (ROP) risk remains largely unexplored.…
cs.AI2025
SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation
Huimin Xu, Xin Mao, Feng-Lin Li +4
Process Reward Models (PRMs) have demonstrated promising results in mathematical reasoning, but existing process annotation approaches, whether through human annotations or Monte C…