Chat models, such as ChatGPT, have shown impressive capabilities and have been rapidly adopted across numerous domains. However, these models are only accessible through a restricted API, creating barriers for new research and progress in the field. We propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself. Subsequently, we employ parameter-efficient tuning to enhance LLaMA, an open-source large language model. The resulting model, named Baize, demonstrates good performance in multi-turn dialogues with guardrails that minimize potential risks. Furthermore, we propose a new technique called Self-Distill with Feedback, to further improve the performance of the Baize models with feedback from ChatGPT. The Baize models and data are released for research purposes only at https://github.com/project-baize/baize-chatbot. An online demo is also available at https://huggingface.co/spaces/project-baize/chat-with-baize.
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Soochow University（苏州大学）；University of Science and Technology（科技大学）
An Empirical Study of Instruction-tuning Large Language Models in Chinese
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；APUS AiLMe Lab（APUS AiLMe实验室）
HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
University of California, Los Angeles（加州大学洛杉矶分校）；Nanyang Technological University（南洋理工大学）；University of California, Davis（加州大学戴维斯分校）；National University of Singapore（新加坡国立大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4
Singapore National Eye Centre（新加坡全国眼科中心）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore Health Services（新加坡保健服务）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore General Hospital（新加坡中央医院）
SparseDoctor: Towards Efficient Chat Doctor with Mixture of Experts Enhanced Large Language Models
Department of Computer Science, Hong Kong Chu Hai College（香港 Chu Hai 学院计算机科学系）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
Optimizing and Fine-tuning Large Language Model for Urban Renewal
Tsinghua University（清华大学）；Cross-strait Tsinghua Research Institute（海峡两岸清华研究院）；Biocloo Inc（百奥科洛公司）；China Agricultural University（中国农业大学）；Zhipu AI（智谱AI）；Beijing Rongchuang Geotechnical Engineering Co., Ltd.（北京融创岩土工程有限公司）；The University of Hong Kong（香港大学）
The Application of ChatGPT in Responding to Questions Related to the Boston Bowel Preparation Scale
First Hospital of Quanzhou Affiliated to Fujian Medical University（福建医科大学附属泉州第一医院）；McGill University（麦吉尔大学）；Montreal Neurological Institute（蒙特利尔神经学研究所）；Mila - Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection
The Graduate University for Advanced Studies, SOKENDAI（总合研究大学院大学（ Sokendai））；National Institute of Informatics（情报通信研究机构信息学研究所）；The University of Tokyo（东京大学）
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；LibrAI；Monash University（莫纳什大学）；University of British Columbia（不列颠哥伦比亚大学）；The University of Melbourne（墨尔本大学）
Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning
Qiannan Normal University for Nationalities（黔南民族师范学院）；Guangzhou University（广州大学）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Technology（北京理工大学）
Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data
Electrical and Computer Engineering, Queen’s University（皇后大学电气与计算机工程学院）；Mechanical and Materials Engineering, Queen’s University（皇后大学机械与材料工程学院）；Ingenuity Labs Research Institute, Queen’s University（皇后大学创新实验室研究 institute）
