While Large Language Models (LLMs) have made significant strides in replicating human-like abilities, there are concerns about a reduction in the linguistic diversity of their outputs. This results in the homogenization of viewpoints and perspectives, as well as the underrepresentation of specific demographic groups. Although several fine-tuning and prompting techniques have been suggested to tackle the issue, they are often tailored to specific tasks or come with a substantial increase in computational cost and latency. This makes them challenging to apply to applications that demand very low latency, such as chatbots and virtual assistants. We propose Possibility Exploration Fine-Tuning (PEFT), a task-agnostic framework that enhances the text diversity of LLMs without increasing latency or computational cost. Given the same prompt, models fine-tuned with PEFT can simultaneously generate multiple diverse responses, each corresponding with a controllable possibility number. Experiments on dialogue and story generation tasks demonstrate that PEFT significantly enhances the diversity of LLM outputs, as evidenced by lower similarity between candidate responses. Since PEFT emphasizes semantic diversity over lexical diversity, it can also notably reduce demographic bias in dialogue systems. The implementations and datasets are available in our repository: https://github.com/mailong25/peft_diversity
Cross-linguistic disagreement as a conflict of semantic alignment norms in multilingual AI~Linguistic Diversity as a Problem for Philosophy, Cognitive Science, and AI~
Japan Advanced Institute of Science and Technology（日本先进科学技术学院）；Victoria University of Wellington（惠灵顿维多利亚大学）；Macquarie University（麦考瑞大学）；Nanzan University（南山大学）；Kyoto University（京都大学）
SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD与CG国家重点实验室）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；School of Software Technology, Zhejiang University（浙江大学软件技术学院）
TrackList: Tracing Back Query Linguistic Diversity for Head and Tail Knowledge in Open Large Language Models
ATILF, University of Lorraine - CNRS, France（ATILF，洛林大学 - CNRS，法国）；Center for Data Science in Humanities, Chosun University, South Korea（人文数据科学中心，全州大学，韩国）
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
School of Computing, The University of Georgia（佐治亚大学计算机学院）；Department of Mathematical and Statistical Sciences, University of Alberta（阿尔伯塔大学数学与统计科学系）；University of California, Los Angeles（加州大学洛杉矶分校）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院放射科和哈佛医学院）
DSDR: Dual-Scale Diversity Regularization for Exploration in LLM Reasoning
The Ohio State University, USA（俄亥俄州立大学）；Case Western Reserve University, USA（凯斯西储大学）；The Chinese University of Hong Kong, Hong Kong（香港中文大学）；Macquarie University, Australia（麦考瑞大学）；University of Michigan, USA（密歇根大学）；The University of Hong Kong, Hong Kong（香港大学）；University College London, UK（伦敦大学学院）
Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Department of Linguistics, Harvard University（哈佛大学语言学系）；Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University（哈佛大学凯普纳自然与人工智能研究 institute）
Research Centre for Cognitive Science & Artificial Intelligence（认知科学与人工智能研究中心）；Department of Computational Cognitive Science（计算认知科学系）；Tilburg University（蒂尔堡大学）
Are LLMs Stable Formal Logic Translators in Logical Reasoning Across Linguistically Diversified Texts?
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）
How desirable is alignment between LLMs and linguistically diverse human users?
Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin School of Mind and Brain（柏林心智与大脑学院）；Einstein Center for Neurosciences Berlin（柏林爱因斯坦神经科学中心）；Technische Universität Berlin（柏林工业大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text
École Polytechnique, Institut Polytechnique de Paris（巴黎综合理工学院、巴黎理工学院）；Linagora（Linagora公司）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Inria（法国国家信息与自动化研究所）
AI Nushu: An Exploration of Language Emergence in Sisterhood -Through the Lens of Computational Linguistics
Royal College of Art（皇家艺术学院）；Tsinghua University（清华大学）；Politecnico di Milano（米兰理工大学）；Hong Kong University of Science and Technology（香港科技大学）；Ubisoft（育碧）；New Drama Studio（新戏剧工作室）；University of London（伦敦大学）；King’s College London（伦敦国王学院）
Limited Linguistic Diversity in Embodied AI Datasets
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Institute of Computer Science, University of Tartu（塔尔图大学计算机科学学院）；Department of Mechanical Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校机械工程系）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
