Qing Yang

Executive Chairman, Du Xiaoman Financial Technology Committee

Executive Chairman of the Technology Committee, General Manager of Data Intelligence Application Department of DuXiaoman Financial, and Head of AI Lab of DuXiaoman, graduated from the Department of Computer Science of Tsinghua University with a master's degree, and worked for Baidu and Alibaba, engaging in R&D in natural language processing, search, recommendation, big data architecture and other related directions. In early 2018, he joined DSM Finance, set up the Data Intelligence Department and AI Lab team, and built the core algorithms of DSM Finance's intelligence engine from 0 to 1. He has been deeply engaged in the technical fields of computer vision, natural language processing, graphical modelling, machine learning, causal inference, etc. A number of articles have been collected by international conferences such as EMNLP, ACL, CIKM, etc., and the "Intelligent The "Intelligent Credit Interpretation Centre" project won the Wu Wenjun Artificial Intelligence Science and Technology Progress Award. The related technologies are widely used in the whole process of marketing, operation, risk control and anti-fraud business scenarios, providing stable and safe financial services for tens of millions of customers. Currently, he focuses on the application of artificial intelligence in financial scenarios, and actively explores the application of generative big language model transformation based on the model-as-a-service (MaaS) model exploration of Du Xiaoman. At the beginning of 2023, he led the team to release "Xuanyuan", a Chinese large language model with hundreds of billions of parameters, and in September 2023, "Xuanyuan-70B" large language model was ranked the top of all the open source models on the C-Eval and CMMLU lists, and has been open-sourced for 6B. In September 2023, "Xuanyuan-70B" was ranked the top of all open source models in C-Eval and CMMLU, and now it has open source 6B/13B/70B/176B full-parameter model system, and authored the book <<Large Language Models: Principles and Engineering Practices.