Large Language Models are expressive tools that enable complex tasks of text understanding within Computational Social Science. Their versatility, while beneficial, poses a barrier for establishing standardized best practices within the field. To bring clarity on the values of different strategies, we present an overview of the performance of modern LLM-based classification methods on a benchmark of 23 social knowledge tasks. Our results point to three best practices: select models with larger vocabulary and pre-training corpora; avoid simple zero-shot in favor of AI-enhanced prompting; fine-tune on task-specific data, and consider more complex forms instruction-tuning on multiple datasets only when only training data is more abundant.
TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generation
Nara Institute of Science and Technology（奈良科学技術研究所）；The University of Tokyo（东京大学）；Chungnam National University（Chungnam国立大学）；Institute of Science Tokyo（东京科学研究所）
In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement
Center for Advanced Intelligence Project, RIKEN（RIKEN先进智能项目中心）；Northeastern University（东北大学）；UNC-Chapel Hill（北卡罗来纳大学教堂山分校）；Center for Advanced Intelligence Project, RIKEN Graduate School of Frontier Sciences, The University of Tokyo（RIKEN前沿科学研究生院、东京大学先进智能项目中心）
Iterative Prompt Refinement for Radiation Oncology Symptom Extraction Using Teacher-Student Large Language Models
Michigan State University（密歇根州立大学）；Henry Ford Cancer Institute（亨利·福特癌症研究所）；Alexandria University（亚历山大大学）；Oakland University（奥克兰大学）；Cedars Sinai Medical Center（西达赛奈医疗中心）
Image-of-Thought Prompting for Visual Reasoning Refinement in Multimodal Large Language Models
School of Engineering, Westlake University（西湖大学工程学院）；City University of Hong Kong（香港城市大学）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；Johns Hopkins University（约翰斯·霍普金斯大学）
PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency
SenseTime Research（商汤科技研究院）；Peking University（北京大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机学院）；University of Cologne（科隆大学）
Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands
Computer Science, University of Massachusetts, Boston, MA, USA（计算机科学，马萨诸塞大学，波士顿，马萨诸塞州，美国）；School of Medicine, University of North Carolina, Chapel Hill, NC, USA（医学院，北卡罗来纳大学，夏洛特，北卡罗来纳州，美国）
Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices
Geneva University Hospitals（日内瓦大学医院）；University of Geneva（日内瓦大学）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）；Université Sorbonne Paris-Nord（索邦巴黎北大学）
Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization
Centre for Software Excellence, Huawei, Canada（华为加拿大软件卓越中心）；Department of Computer Science, University of Manitoba, Canada（曼尼托巴大学计算机科学系）；School of Computing, Queen’s University, Canada（皇后大学计算学院）
Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；International School, Beijing University of Posts and Telecommunications（北京邮电大学国际学院）；Alibaba Group（阿里巴巴集团）
