6 papers
C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts
Chenxi Qing, Junxi Wu, Zheng Liu +5
Recently, large language models (LLMs) are capable of generating highly fluent textual content. While they offer significant convenience to humans, they also introduce various risk…
Alignment Imprint: Zero-Shot AI-Generated Text Detection via Provable Preference Discrepancy
Junxi Wu, Kailin Huang, Dongjian Hu +4
Detecting AI-generated text is an important but challenging problem. Existing likelihood-based detection methods are often sensitive to content complexity and may exhibit unstable…
Conversations Risk Detection LLMs in Financial Agents via Multi-Stage Generative Rollout
Xiaotong Jiang, Jun Wu
With the rapid adoption of large language models (LLMs) in financial service scenarios, dialogue security detection under high regulatory risk presents significant challenges. Exis…
Distribution-informed Online Conformal Prediction
Dongjian Hu, Junxi Wu, Shu-Tao Xia +1
Conformal prediction provides a pivotal and flexible technique for uncertainty quantification by constructing prediction sets with a predefined coverage rate. Many online conformal…
MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds
Junxi Wu, Jinpeng Wang, Zheng Liu +4
The rapid advancement of large language models has intensified public concerns about the potential misuse. Therefore, it is important to build trustworthy AI-generated text detecti…
Error-quantified Conformal Inference for Time Series
Junxi Wu, Dongjian Hu, Yajie Bao +2
Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. Conformal inference provides a pivotal…