用检索增强生成技术，让大模型更专业地写采购文件。
A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation
- 引入检索增强机制，提升大模型在采购文档中的专业知识
- 解决通用大模型在专业领域知识不足的问题
- 适合需要高质量采购文档生成的政府与企业用户
采购文档的起草日益复杂多样，需满足法律要求、技术进步及利益相关方需求。尽管大语言模型（LLMs）在文档生成方面展现出潜力，但多数缺乏采购领域的专业知识。为弥补这一差距，本文采用检索增强技术实现专业文档生成，确保采购文档的准确性和相关性。
原文摘要 · Abstract (English)
The drafting of documents in the procurement field has progressively become more complex and diverse, driven by the need to meet legal requirements, adapt to technological advancements, and address stakeholder demands. While large language models (LLMs) show potential in document generation, most LLMs lack specialized knowledge in procurement. To address this gap, we use retrieval-augmented techniques to achieve professional document generation, ensuring accuracy and relevance in procurement documentation.
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