REVIEW 1 cited by
A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
MSRS provides two multi-source retrieval and synthesis benchmarks and shows generation quality depends heavily on retrieval, with reasoning models best at oracle synthesis.
Discussion (0). Continue with ORCID to comment.