2 citations · 2 across the 5 of their papers we have counts for
5 papers
Task Abstention for Large Language Models in Code Generation
Yanke Zhou, Yuhao Tan, Senrong Xu +4
Large language models (LLMs) have revolutionized automated code generation. One serious concern, however, is the so-called ``hallucination'', i.e., LLMs may generate seemingly plau…
Uncertainty Quantification for LLM-based Code Generation
Senrong Xu, Yuhao Tan, Yanke Zhou +6
Prediction sets provide a theoretically grounded framework for quantifying uncertainty in machine learning models. Adapting them to structured generation tasks, in particular, larg…
Fair Conformal Classification via Learning Representation-Based Groups
Senrong Xu, Yanke Zhou, Yuhao Tan +5
Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to account for algorithmic biases, th…
Conformal Correction for Efficiency May be at Odds with Entropy
Senrong Xu, Tianyu Wang, Zenan Li +4
Conformal prediction (CP) provides a comprehensive framework to produce statistically rigorous uncertainty sets for black-box machine learning models. To further improve the effici…
Detecting Topology Attacks against Graph Neural Networks
Senrong Xu, Yuan Yao, Liangyue Li +3
Graph neural networks (GNNs) have been widely used in many real applications, and recent studies have revealed their vulnerabilities against topology attacks. To address this issue…