Recent advancements in LLMs have showcased their remarkable role-playing capabilities, able to accurately simulate the dialogue styles and cognitive processes of various roles based on different instructions and contexts. Studies indicate that assigning LLMs the roles of experts, a strategy known as role-play prompting, can enhance their performance in the corresponding domains. However, the prompt needs to be manually designed for the given problem, requiring certain expertise and iterative modifications. To this end, we propose self-prompt tuning, making LLMs themselves generate role-play prompts through fine-tuning. Leveraging the LIMA dataset as our foundational corpus, we employ GPT-4 to annotate role-play prompts for each data points, resulting in the creation of the LIMA-Role dataset. We then fine-tune LLMs like Llama-2-7B and Mistral-7B on LIMA-Role. Consequently, the self-prompt tuned LLMs can automatically generate expert role prompts for any given question. We extensively evaluate self-prompt tuned LLMs on widely used NLP benchmarks and open-ended question test. Our empirical results illustrate that self-prompt tuned LLMs outperform standard instruction tuned baselines across most datasets. This highlights the great potential of utilizing fine-tuning to enable LLMs to self-prompt, thereby automating complex prompting strategies. We release the dataset, models, and code at this \href{https://anonymous.4open.science/r/Self-Prompt-Tuning-739E/}{url}.
Improving LLM Reasoning through Interpretable Role-Playing Steering
Center for Information and Language Processing, LMU Munich（信息与语言处理中心，慕尼黑大学）；Northwestern University（西北大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；New Jersey Institute of Technology（新泽西理工学院）
!MSA at AraHealthQA 2025 Shared Task: Enhancing LLM Performance for Arabic Clinical Question Answering through Prompt Engineering and Ensemble Learning
Faculty of Computer Science, MSA University（计算机科学学院，MSA大学）
AvalonBench: Evaluating LLMs Playing the Game of Avalon
University of California, Berkeley（加州大学伯克利分校）；California Institute of Technology（加州理工学院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Shenzhen University（深圳大学）
PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models
Institute of Data Science（数据科学研究所）；NUS Centre for Trusted Internet and Community, National University of Singapore（新加坡国立大学可信互联网与社区中心）；Amazon（亚马逊）；Stanford University（斯坦福大学）；Tsinghua University（清华大学）
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
Beijing University of Posts and Telecommunications（北京邮电大学）；The Hong Kong University of Science and Technology（香港科技大学）；Beihang University（北京航空航天大学）；University of the Chinese Academy of Sciences（中国科学院大学）；ETH Zürich（苏黎世联邦理工学院）；The Hong Kong Polytechnic University（香港理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shanghai AI Lab（上海人工智能实验室）
Dissecting Role Cognition in Medical LLMs via Neuronal Ablation
School of Information, Renmin University of China（中国人民大学信息学院）；Center of Machine Learning Research, Peking University（北京大学机器学习研究中心）；School of Artificial Intelligence, Shanghai Jiaotong University（上海交通大学人工智能学院）；Health Informatics Committee of the China Society for Scientific and Technical Information, Renmin University of China（中国科学技术信息学会健康信息技术委员会，中国人民大学）；Institute for Advanced Algorithms Research (IAAR), Shanghai, China（先进算法研究所（IAAR），上海，中国）；Memtensor Research Center, Shanghai, China（Memtensor研究中心，上海，中国）
Beyond One World: Benchmarking Super Heros in Role-Playing Across Multiversal Contexts
Faculty of Engineering, Thammasat School of Engineering, Thammasat University（工程学院，泰国朱拉安高校工程学院，泰国朱拉安大学）；Department of Computer Engineering and Digital Technology, Faculty of Engineering, Chulalongkorn University（计算机工程与数字技术系，工程学院，朱拉安大学）；Artificial Intelligence Association of Thailand（泰国人工智能协会）；School of Biomedical Engineering & Imaging Sciences, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）；Siriraj Informatics and Data Innovation Center (SIData+), Faculty of Medicine, Siriraj Hospital, Mahidol University（Siriraj信息与数据创新中心（SIData+），医学学院，Siriraj医院，玛希道大学）
本文提出Beyond One World基准，通过30位英雄、90个正典版本及正典事件与道德困境任务，评估LLM跨多元宇宙角色一致性，并以Think-Act Matching衡量推理与行动对齐。
FURINA: A Fully Customizable Role-Playing Benchmark via Scalable Multi-Agent Collaboration Pipeline
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The University of Hong Kong（香港大学）；Stony Brook University（石溪大学）；Nanyang Technological University（南洋理工大学）；Datawhale Org.（Datawhale 组织）；Independent Researcher（独立研究者）
RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；School of Cyber Science and Technology, Zhejiang University（浙江大学网络科学与技术学院）；Palo Alto Networks（派拓网络）；University of North Texas（北得克萨斯大学）
Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing
Computational Linguistics and Text Mining Lab（计算语言学与文本挖掘实验室）；HK3Lab（HK3实验室）；LT3, Language and Translation Technology Team（LT3语言与翻译技术团队）；Dipartimento di Informatica（信息学院）；aequa-tech（aequa-tech公司）；Fundamentals of Natural Language Processing（自然语言处理基础）
PHASE-Tree: Modeling Character-State Evolution in Long-Horizon Role-Playing Dialogue
MemTensor (Shanghai) Technology（MemTensor（上海）科技）；KU Leuven（鲁汶大学）；Zhejiang University（浙江大学）；University of Chinese Academy of Sciences（中国科学院大学）；Sinar Mas Paper (China) Investment Co., Ltd（金光纸业（中国）投资有限公司）；The Hong Kong Polytechnic University（香港理工大学）
