mlsanityData to 7 Oct 2026

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

arXiv 2410.09077 ↗First posted 4 Oct 2024cs.CL cs.IRcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

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.

Key facts. Cited by 1 paper in the map (data through 7 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.

Builds on

5 of 9 references.

References · 5

  1. 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 691
  2. 2021Neural Machine Translation with Monolingual Translation Memorycited by 61
  3. 2021Adaptive Nearest Neighbor Machine Translationcited by 58
  4. 2024LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domaincited by 9
  5. 2024Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Modelcited by 5

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025MSRS: Evaluating Multi-Source Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documents
  2. 2025Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance
  3. 2026Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study
  4. 2025CReSt: A Comprehensive Benchmark for Retrieval-Augmented Generation with Complex Reasoning over Structured Documents
  5. 2025Streamlining Industrial Contract Management with Retrieval-Augmented LLMs
  6. 2024Deploying Large Language Models With Retrieval Augmented Generation
  7. 2025Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding
  8. 2025Metadata Extraction Leveraging Large Language Models