paper-with-me

홈 › Papers

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

2024-10-04 · Yilong Zhao, Daifeng Li

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.

📄 PDF Abstract BibTeX arXiv:2410.09077

Code (0)

등록된 구현이 없습니다.

Tasks

Language ModelingLanguage ModellingLarge Language ModelRetrievalRetrieval-augmented Generation

Similar Papers 제목 키워드 기반

The Extender: A Log-Structured Transformer

2026-09-26 · Jakob Eriksson hf

We introduce the Extender, a log-structured variant of the standard Transformer architecture. In a standard Transformer, each layer communicates with subsequent layers exclusively via the residual h, a superposition chan…

PandaPy: A Wrapper Around Structured Arrays to Mimic ‘Structs’ in the C Language

2020-05-13 · The Alan Turing Institute 2020 5 · Derek Snow

Similar to the original Pandas project, PandaPy is developed to improve the usability of python for finance. Structured data types are designed to be able to mimic ‘structs’ in the C language, and they share a similar me…

Language Models can Self-Lengthen to Generate Long Texts

2024-10-31 · Shanghaoran Quan, Tianyi Tang, Bowen Yu, An Yang 외

Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned outputs. This limitation stems from a training…

Text Generation

HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Data

2025-08-25 · Jiyoon Myung, Jihyeon Park, Joohyung Han arxiv

User queries in real-world recommendation systems often combine structured constraints (e.g., category, attributes) with unstructured preferences (e.g., product descriptions or reviews). We introduce HyST (Hybrid retriev…

Recommendation Systems

Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence

2024-12-01 · Qianren Mao, Weifeng Jiang, Junnan Liu, Chenghua Lin 외

The semi-supervised learning (SSL) strategy in lightweight models requires reducing annotated samples and facilitating cost-effective inference. However, the constraint on model parameters, imposed by the scarcity of tra…

text-classificationText Classification