Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
George Mason University（乔治梅森大学）；GE Healthcare（GE医疗）；University of Pittsburgh（匹兹堡大学）；William and Mary（威廉玛丽学院）；Southern University of Science and Technology（南方科技大学）；Florida State University（佛罗里达州立大学）
Zhepeng Wang, Runxue Bao, Yawen Wu, Jackson Taylor, Cao Xiao, Feng Zheng, Weiwen Jiang, Shangqian Gao, Yanfu Zhang
Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation. However, LLMs pose significant security risks due to their tendency to memorize training data, leading to potential privacy breaches and copyright infringement. Accurate measurement of this memorization is essential to evaluate and mitigate these potential risks. However, previous attempts to characterize memorization are constrained by either using prefixes only or by prepending a constant soft prompt to the prefixes, which cannot react to changes in input. To address this challenge, we propose a novel method for estimating LLM memorization using dynamic, prefix-dependent soft prompts. Our approach involves training a transformer-based generator to produce soft prompts that adapt to changes in input, thereby enabling more accurate extraction of memorized data. Our method not only addresses the limitations of previous methods but also demonstrates superior performance in diverse experimental settings compared to state-of-the-art techniques. In particular, our method can achieve the maximum relative improvement of 112.75% and 32.26% over the vanilla baseline in terms of discoverable memorization rate for the text generation task and code generation task respectively.
From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions
City University of Hong Kong（香港城市大学）；Zhejiang Normal University（浙江师范大学）；Squirrel Ai Learning；University of Science and Technology of China（中国科学技术大学）；Wuhan University（武汉大学）
Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs
Zhejiang Gongshang University（浙江工商大学）；Zhejiang University（浙江大学）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；Harbin Institute of Technology（哈尔滨工业大学）
Retrieval-augmented Prompt Learning for Pre-trained Foundation Models
MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（信息产业部模式分析与机器智能重点实验室，计算机科学与技术学院，南京航空航天大学）；Zhejiang University（浙江大学）；Hunan Vanguard Group Corporation Limited（湖南先锋集团有限公司）；National University of Singapore（新加坡国立大学）
Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Institute for Artificial Intelligence（人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）
ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Automation, Tsinghua University（清华大学自动化系）；Taizhou University（台州大学）；Anhui University（安徽大学）；Beijing National Research Center for Information Science and Technology,Tsinghua University（北京信息科学与技术国家研究中心，清华大学）
Compiler-Guided Inference-Time Adaptation: Improving GPT-5 Programming Performance in Idris
Ming Hsieh Department of Electrical and Computer Engineering（明希斯部门电子与计算机工程系）；Viterbi School of Engineering, University of Southern California（维特比工程学院，南加州大学）
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
BNU-HKBU United International College（北师港浸大）；Hong Kong Baptist University（香港浸会大学）；Beijing Normal University at Zhuhai（北京师范大学珠海校区）；The Chinese University of Hong Kong（香港中文大学）
Do Multimodal Large Language Models Understand Welding?
University of Notre Dame（圣母大学）；Keough School of Global Affairs, University of Notre Dame（圣母大学基全球事务学院）；Industry Labs, University of Notre Dame（圣母大学产业实验室）
Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning
Tencent BAC（腾讯BAC）；Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学学院）
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks
Pine AI；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Duke University（杜克大学）；University of Michigan（密歇根大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Johns Hopkins University（约翰霍普金斯大学）；University of Notre Dame（圣母大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
Beyond Reasoning: Reinforcement Learning Unlocks Parametric Knowledge in LLMs
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS University of Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院，中国科学院大学）
