DALD: Improving Logits-based Detector without Logits from Black-box LLMs
MBZUAI（Mohamed bin Zayed 人工智能大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of California, Los Angeles（加州大学洛杉矶分校）；NEC Labs America（美国 NEC 实验室）；University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）；NC State University（北卡罗来纳州立大学）
The advent of Large Language Models (LLMs) has revolutionized text generation, producing outputs that closely mimic human writing. This blurring of lines between machine- and human-written text presents new challenges in distinguishing one from the other a task further complicated by the frequent updates and closed nature of leading proprietary LLMs. Traditional logits-based detection methods leverage surrogate models for identifying LLM-generated content when the exact logits are unavailable from black-box LLMs. However, these methods grapple with the misalignment between the distributions of the surrogate and the often undisclosed target models, leading to performance degradation, particularly with the introduction of new, closed-source models. Furthermore, while current methodologies are generally effective when the source model is identified, they falter in scenarios where the model version remains unknown, or the test set comprises outputs from various source models. To address these limitations, we present Distribution-Aligned LLMs Detection (DALD), an innovative framework that redefines the state-of-the-art performance in black-box text detection even without logits from source LLMs. DALD is designed to align the surrogate model's distribution with that of unknown target LLMs, ensuring enhanced detection capability and resilience against rapid model iterations with minimal training investment. By leveraging corpus samples from publicly accessible outputs of advanced models such as ChatGPT, GPT-4 and Claude-3, DALD fine-tunes surrogate models to synchronize with unknown source model distributions effectively.
Leave No TRACE: Black-box Detection of Copyrighted Dataset Usage in Large Language Models via Watermarking
Antiquus S. Hippocampus, Natalia Cerebro & Amelie P. Amygdale Department of Computer Science Cranberry-Lemon University Pittsburgh, PA 15213, USA（计算机科学系，Cranberry-Lemon大学）；Ji Q. Ren & Yevgeny LeNet Department of Computational Neuroscience University of the Witwatersrand Joburg, South Africa（计算神经科学系，沃特瓦特斯兰大学）
Department of School of Engineering, Shenzhen MSU-BIT University, Shenzhen, China, 518000（深圳MSU-BIT大学工程学院部门）；MSU-BIT-SMBU Joint Research Center of Applied Mathematics, Shenzhen MSU-BIT University, Shenzhen, China, 518000（应用数学联合研究中心）
Confidence Elicitation: A New Attack Vector for Large Language Models
National University of Singapore（新加坡国立大学）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究所）；Centre for Frontier AI Research, A*STAR（新加坡科技研究局前沿人工智能研究中心）
SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models
Shenzhen Key Laboratory for High Performance Data Mining（深圳高性能数据挖掘重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Shenzhen University（深圳大学）；University of Science and Technology of China（中国科学技术大学）；PolyU（香港理工大学）；East China Normal University（华东师范大学）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳大学先进技术研究院人工智能研究所）；University of New South Wales（新南威尔士大学）；Anhui University（安徽大学）
Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL
Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；University of Chinese Academy of Sciences（中国科学院大学）
