The field of privacy-preserving Natural Language Processing has risen in popularity, particularly at a time when concerns about privacy grow with the proliferation of Large Language Models. One solution consistently appearing in recent literature has been the integration of Differential Privacy (DP) into NLP techniques. In this paper, we take these approaches into critical view, discussing the restrictions that DP integration imposes, as well as bring to light the challenges that such restrictions entail. To accomplish this, we focus on $\textbf{DP-Prompt}$, a recent method for text privatization leveraging language models to rewrite texts. In particular, we explore this rewriting task in multiple scenarios, both with DP and without DP. To drive the discussion on the merits of DP in NLP, we conduct empirical utility and privacy experiments. Our results demonstrate the need for more discussion on the usability of DP in NLP and its benefits over non-DP approaches.
Rewarding How Models Think Pedagogically: Integrating Pedagogical Reasoning and Thinking Rewards for LLMs in Education
Chosun University（昌原大学）；Korea University（韩国大学）；Seoul National University（首尔国立大学）；Korea Institute for Curriculum and Evaluation（韩国课程与评价研究院）；Upstage；Indiana University Bloomington（印第安纳大学布卢明顿分校）；Nanyang Technological University（南洋理工大学）
SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education（大数据与人工智能交通 key laboratory（北京交通大学））；School of Computer Science and Technology, Beijing Jiaotong University（计算机科学与技术学院（北京交通大学））；University of Montreal（蒙特利尔大学）
Toward LLM-Supported Automated Assessment of Critical Thinking Subskills
University of Minnesota（明尼苏达大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Adelaide University（阿德莱德大学）；Brighter Research（Brighter研究公司）；Educational Testing Service (ETS)（教育考试服务中心（ETS））
AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering
Computer Science, Fudan University（复旦大学计算机科学系）；Institute of Modern Languages and Linguistics, Fudan University（复旦大学现代语言与语言学研究院）；Shanghai Innovation Institute（上海创新研究院）；Soochow University（苏州大学）
Multi-Persona Thinking for Bias Mitigation in Large Language Models
Dept. Computing Science, Alberta Machine Intelligence Institute (Amii) University of Alberta, Canada（计算科学系，阿尔伯塔机器智能研究所（Amii），阿尔伯塔大学，加拿大）；Canada CIFAR AI Chair, Amii（加拿大 CIFAR 人工智能主席，Amii）
Thinking Less to Simulate Better: Intuitive Prompting Improves LLM Agents Simulating Individual Social Media Reactions, Including Unfamiliar Content
Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研发研究所）；University of Belgrade（贝尔格莱德大学）；Institute for Philosophy and Social Theory（哲学与社会理论研究所）；Faculty of Political Sciences（政治科学学院）；University of Vienna（维也纳大学）；Faculty of Social Sciences（社会科学学院）；Universitas Negeri Padang（巴东国立大学）；Faculty of Engineering（工程学院）；University of Novi Sad（诺维萨德大学）；Faculty of Philosophy（哲学学院）；Nanyang Technological University（南洋理工大学）；School of Social Sciences（社会科学学院）
Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey
University of Oklahoma（俄克拉荷马大学）；Stanford University（斯坦福大学）；School of Electrical and Computer Engineering, Oklahoma State University（电气与计算机工程学院，俄克拉荷马州立大学）；University of Glasgow（格拉斯哥大学）
Chain of Grounded Objectives: Bridging Process and Goal-oriented Prompting for Code Generation
Electronics and Telecommunications Research Institute（电子电信研究院）；Department of Computer Science and Engineering, Seoul National University（计算机科学与工程系，首尔国立大学）
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
Department of Computer Science, Princeton University, Princeton, NJ, USA（计算机科学系，普林斯顿大学）；NYU Center for Data Science, New York University, New York, NY, USA（纽约大学数据科学中心）；Department of Psychology, Princeton University, Princeton, NJ, USA（心理学系，普林斯顿大学）
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning
School of Computer Science, Wuhan University（武汉大学计算机学院）；National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；School of Electronic Information Engineering, Henan Polytechnic Institute（河南工业职业技术学院电子信息工程学院）；JD Explore Academy（京东探索研究院）
SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Southern California（南加州大学）；University of Washington（华盛顿大学）；Tsinghua University（清华大学）；Paradigm Inc.（第四范式公司）；University of California, San Diego（加州大学圣地亚哥分校）；MosaicML（MosaicML公司）
