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cs.CL2025
TECP: Token-Entropy Conformal Prediction for LLMs
Beining Xu, Yongming Lu
Uncertainty quantification (UQ) for open-ended language generation remains a critical yet underexplored challenge, especially under black-box constraints where internal model signa…
cs.CL2025
Understanding the Effects of RLHF on the Quality and Detectability of LLM-Generated Texts
Beining Xu, Arkaitz Zubiaga
Large Language Models (LLMs) have demonstrated exceptional performance on a range of downstream NLP tasks by generating text that closely resembles human writing. However, the ease…
cs.CL2023★ 1 cited
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors
Shuyue Stella Li, Beining Xu, Xiangyu Zhang +3
In this work, we study the features extracted by English self-supervised learning (SSL) models in cross-lingual contexts and propose a new metric to predict the quality of feature…