7 papers
DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection
Guoxin Ma, Xiaoming Liu, Hongyang Chen +6
Detecting machine-generated text has become a critical challenge amid the rapid advancement of LLMs, yet existing detectors degrade severely under domain shift. Through systematic…
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression
Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang +5
Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance. Existing w…
MGTEVAL: An Interactive Platform for Systemtic Evaluation of Machine-Generated Text Detectors
Yuanfan Li, Qi Zhou, Chengzhengxu Li +5
We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detection, existing evaluations are of…
Confidence Should Be Calibrated More Than One Turn Deep
Zhaohan Zhang, Chengzhengxu Li, Xiaoming Liu +3
Large Language Models (LLMs) are increasingly applied in high-stakes domains such as finance, healthcare, and education, where reliable multi-turn interactions with users are essen…
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training
Yuanfan Li, Zhaohan Zhang, Chengzhengxu Li +2
Machine-generated Text (MGT) detection is crucial for regulating and attributing online texts. While the existing MGT detectors achieve strong performance, they remain vulnerable t…
Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language Models
Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang +4
Recent advances in prompt optimization have notably enhanced the performance of pre-trained language models (PLMs) on downstream tasks. However, the potential of optimized prompts…