# A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2410.09077 · HTML: https://mlsanity.com/paper/2410.09077/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 5 of 5)

- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Neural Machine Translation with Monolingual Translation Memory](https://mlsanity.com/paper/2105.11269/) (2021)
- [Adaptive Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.13022/) (2021)
- [LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain](https://mlsanity.com/paper/2402.14871/) (2024)
- [Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model](https://mlsanity.com/paper/2406.04202/) (2024)

## Built on by (most cited first, 1 of 1)

- [MSRS: Evaluating Multi-Source Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.20867/) (2025)

## Abstract (arXiv)

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.