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| Standardizing Document Generation Based on Large Language Models |
| LIU Zheze1,2, ZHANG Ning3, ZHENG Nan1,4
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1. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
2. School of Cryptography and Cyberspace Security, Nankai University, Tianjin 300350, China;
3.Institute of Forensic Science Ministry of Public Security, Beijing 100038, China;
4. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract In order to promote the standardized development of various industries, corresponding standardizing documents need to be formulated in various fields, such as national standard and industry standard. These standardizing documents not only provide a unified operating standard for the industry, but also provide a clear guidance basis for relevant parties. The Central Committee of the CPC and the State Council clearly pointed out in the "the Outlines for the Development of National Standardization" that promoting the digitalization process of standard is an important measure to realize the modernization of the industry. Therefore, it is particularly important to carry out research on the automatic generation of standardizing documents. With the rapid development of artificial intelligence technology, especially the outstanding performance of large language models in text generation tasks, it is possible to use these advanced technologies to realize the automatic generation of standardizing documents. Based on this background, this paper proposes a two-stage scheme for generating standardizing documents. The scheme first generates the outline of the standardizing document through the large model, and then expands to generate the complete document content on this basis. By combining in-context learning and retrieval augmented generation techniques, this method can not only generate high-quality text, but also significantly improve the accuracy and professionalism of the generated content. In order to verify the feasibility of the scheme, we conducted a series of experiments on our self-built dataset, and the results show that the method can effectively generate documents that meet industry standards, and has good practicability and promotion potential.
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Received: 28 March 2025
Published: 03 June 2025
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