Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences（中国科学院智能信息处理重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong University（西安交通大学）
Yuhui Shi, Qiang Sheng, Juan Cao, Hao Mi, Beizhe Hu, Danding Wang
With the rapidly increasing application of large language models (LLMs), their abuse has caused many undesirable societal problems such as fake news, academic dishonesty, and information pollution. This makes AI-generated text (AIGT) detection of great importance. Among existing methods, white-box methods are generally superior to black-box methods in terms of performance and generalizability, but they require access to LLMs' internal states and are not applicable to black-box settings. In this paper, we propose to estimate word generation probabilities as pseudo white-box features via multiple re-sampling to help improve AIGT detection under the black-box setting. Specifically, we design POGER, a proxy-guided efficient re-sampling method, which selects a small subset of representative words (e.g., 10 words) for performing multiple re-sampling in black-box AIGT detection. Experiments on datasets containing texts from humans and seven LLMs show that POGER outperforms all baselines in macro F1 under black-box, partial white-box, and out-of-distribution settings and maintains lower re-sampling costs than its existing counterparts.
Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models
Key Lab of High Confidence Software Technologies (Peking University), Ministry of Education（高可信软件技术教育部重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）
Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation
School of Computer Science and Technology, Tianjin University, China（天津大学计算机科学与技术学院）；School of Future Technology, Tianjin University, China（天津大学未来技术学院）；Georgia Tech Shenzhen Institute, Tianjin University, China（Georgia Tech深圳研究院）；School of Artificial Intelligence, Tianjin University, China（天津大学人工智能学院）
ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs
Department of EECS UC Berkeley（电子工程与计算机科学系，伯克利大学）；Department of Statistics UC Berkeley（统计学系，伯克利大学）；Departments of Statistics and EECS UC Berkeley（统计学系和电子工程与计算机科学系，伯克利大学）
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors
The Pennsylvania State University（宾夕法尼亚州立大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Nanjing University（南京大学）；University of Virginia（弗吉尼亚大学）；NEC Laboratories America（美国NEC实验室）
Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；National Computer Network Emergency Response Technical Team, Coordination Center of China (CNCERT/CC)（国家计算机网络应急技术处理协调中心）
Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems
