Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.09077 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916436725727232 |
|---|---|
| author | Zhao, Yilong Li, Daifeng |
| author_facet | Zhao, Yilong Li, Daifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation Zhao, Yilong Li, Daifeng Computation and Language Information Retrieval 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. |
| title | A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2410.09077 |