[Major Project] Research Team of "Collection, Organization, and Research of Ancient Agricultural Books in China" Holds Quarterly Progress Meeting in Beijing and Releases "Qimin" Ancient Agricultural Language Model

(Correspondent: Gao Zhaoqiang) From September 19 to 20, the research team for the National Social Science Fund major bidding project "Collection, Organization, and Research of Ancient Agricultural Books in China," led by Professor Wu Ping, held a quarterly progress meeting in Beijing and released the "Qimin" ancient agricultural language model. More than thirty experts, scholars, and graduate students attended the meeting.

On the afternoon of the 19th, the research team held its quarterly progress meeting at the Chinese Academy of Agricultural Sciences, hosted by Professor Wu Ping. First, the person in charge of Sub-project Three, Cui Yunpeng, and his team reported on the construction mechanism and research achievements of the "Qimin" ancient agricultural language model as well as its application in the " Open sharing and knowledge service platform for ancient agricultural book resources." Subsequently, other sub-project leaders and key members reported on their research progress and results. Finally, Professor Wu Ping summarized the meeting, emphasizing that each sub-topic should further implement the research tasks based on the project proposal, summarize the unique features of their research, and provide society with comprehensive, systematic, and accurate research outcomes.

On the morning of the 20th, the launch conference for "Qimin" ancient agricultural language model was held at the Chinese Academy of Agricultural Sciences. Researcher Cui Yunpeng provided a detailed introduction to the core functions, technological innovations, and application of this language model. Attending experts and scholars shared their opinions on the model and discussed its future development.

The "Qimin" Ancient Agricultural Language Model is one of the research outcomes of the major project funded by the National Social Science Fund, titled "Collection, Organization, and Research of Ancient Chinese Agricultural Books." This model is trained based on a large number of ancient agricultural texts from China, therefore it has powerful natural language processing capabilities. It can provide precise answers to various agricultural issues related to ancient crop cultivation, livestock breeding, farmland water management, meteorological forecasting, and more. Tools based on this language model, such as automatic text processing and semantic retrieval, offer efficient analytical support for scholars conducting research on ancient society and agriculture.