Controlling the attribute intensity of text generation is crucial across scenarios (e.g., writing conciseness, chatting emotion, and explanation clarity). The remarkable capabilities of large language models (LLMs) have revolutionized text generation, prompting us to explore such \emph{smooth control} of LLM generation. Specifically, we propose metrics to assess the range, calibration, and consistency of the generated text's attribute intensity in response to varying control values, as well as its relevance to the intended context. To quantify the attribute intensity and context relevance, we propose an effective evaluation framework leveraging the Elo rating system and GPT4, both renowned for their robust alignment with human judgment. We look into two viable training-free methods for achieving smooth control of LLMs: (1) Prompting with semantic shifters, and (2) Modifying internal model representations. The evaluations of these two methods are conducted on $5$ different attributes with various models. Our code and dataset can be obtained from \url{https://github.com/ShangDataLab/Smooth-Control}.
Identifying Intensity of the Structure and Content in Tweets and the Discriminative Power of Attributes in Context with Referential Translation Machines
AI Enablement（AI赋能部门）；Huawei Türkiye R&D Center（华为土耳其研发中心）
StyleBench: Evaluating Speech Language Models on Conversational Speaking Style Control
NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China（计算机科学与工程学院自然语言处理实验室，东北大学，沈阳，中国）；NiuTrans Research, Shenyang, China（尼乌透研究，沈阳，中国）
CASTLE: A Comprehensive Benchmark for Evaluating Student-Tailored Personalized Safety in Large Language Models
East China Normal University（华东师范大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Zhejiang University（浙江大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Education University of Hong Kong（香港教育大学）
Mind the Style Gap: Meta-Evaluation of Style and Attribute Transfer Metrics
Department of Computer Science, Aarhus University, Denmark（计算机科学系，奥胡斯大学）；Department of Computer Science, University of Copenhagen, Denmark（计算机科学系，哥本哈根大学）
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm
Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研究与发展研究所）；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）；Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU)（莱茵兰-普法尔茨州 Kaiserslautern-Landau 应用科技大学）；German Research Centre for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Vilnius Gediminas Technical University（维尔纽斯格迪米纳斯技术大学）；Drexel University（德雷塞尔大学）；University of Montenegro（黑山大学）；Faculty of Dramatic Arts（戏剧艺术学院）；University of Kragujevac（克拉古耶瓦茨大学）；Faculty of Engineering（工程学院）
Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs
University of Cambridge（剑桥大学）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Apta（Apta公司）；Royal Holloway, University of London（伦敦大学皇家霍洛威学院）
Emotion Intensity and its Control for Emotional Voice Conversion
National University of Singapore（新加坡国立大学）；Singapore University of Technology and Design（新加坡科技设计大学）；University of Southern Queensland（南昆士兰大学）；Imperial College London（帝国理工学院）；University of Augsburg（奥格斯堡大学）；University of Bremen（不来梅大学）
