Semi-Structured Tender Document Retrieval-Augmented Generation: A Framework Based on Large Language Model

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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Publication Details

Published
2024-08-29
DOI
https://doi.org/10.1109/icsece61636.2024.10729522
Citations
2
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.69
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article

Semi-Structured Tender Document Retrieval-Augmented Generation: A Framework Based on Large Language Model

Daifeng Li, Yilong Zhao
2 citations
Topic Modeling
0.69
article

Semi-Structured Tender Document Retrieval-Augmented Generation: A Framework Based on Large Language Model

Daifeng Li, Yilong Zhao
article en
2 citations

Abstract

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.

Sun Yat-sen University (CN)
Openalex Percentile: Top 24%
Topic Modeling
0.69
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