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On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models
Shuoyuan Wang, Beier Luo, Hao Zeng +5
Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a promising way to optimize ca…
Tail-Aware Top- On-Policy Distillation
Huipeng Huang, Hongxin Wei
On-policy distillation (OPD) has emerged as an effective paradigm for transferring knowledge between language models, where a student is trained to align its next-token distributio…
Provable Model Provenance Set for Large Language Models
Xiaoqi Qiu, Hao Zeng, Zhiyu Hou +1
The growing prevalence of unauthorized model usage and misattribution has increased the need for reliable model provenance analysis. However, existing methods largely rely on heuri…
HyPAC: Cost-Efficient LLMs-Human Hybrid Annotation with PAC Error Guarantees
Hao Zeng, Huipeng Huang, Xinhao Qu +3
Data annotation often involves multiple sources with different cost-quality trade-offs, such as fast large language models (LLMs), slow reasoning models, and human experts. In this…
Distribution-informed Efficient Conformal Prediction for Full Ranking
Wenbo Liao, Huipeng Huang, Chen Jia +3
Quantifying uncertainty is critical for the safe deployment of ranking models in real-world applications. Recent work offers a rigorous solution using conformal prediction in a ful…
Model-agnostic Selective Labeling with Provable Statistical Guarantees
Huipeng Huang, Wenbo Liao, Huajun Xi +3
Obtaining high-quality labels for large datasets is expensive, requiring massive annotations from human experts. While AI models offer a cost-effective alternative by predicting la…
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
Xuanning Zhou, Zihao Shi, Hao Zeng +3
Conformal prediction (CP) is a powerful framework for uncertainty quantification, generating prediction sets with coverage guarantees. Split conformal prediction relies on labeled…
Parametric Scaling Law of Tuning Bias in Conformal Prediction
Hao Zeng, Kangdao Liu, Bingyi Jing +1
Conformal prediction is a popular framework of uncertainty quantification that constructs prediction sets with coverage guarantees. To uphold the exchangeability assumption, many c…
Exploring the Noise Robustness of Online Conformal Prediction
Huajun Xi, Kangdao Liu, Hao Zeng +2
Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability. Rec…
C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets
Kangdao Liu, Hao Zeng, Jianguo Huang +3
Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classi…
TorchCP: A Python Library for Conformal Prediction
Jianguo Huang, Jianqing Song, Xuanning Zhou +2
Conformal prediction (CP) is a powerful statistical framework that generates prediction intervals or sets with guaranteed coverage probability. While CP algorithms have evolved bey…
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints
Xiaobo Xia, Jiale Liu, Shaokun Zhang +3
Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms. It strives to identify a small subset from large-scale…