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Results 11 to 20 of about 3,238,639 (146)

RACE: Retrieval-Augmented Commit Message Generation

, 2022
<p>The dataset of ``RACE: Retrieval-Augmented Commit Message Generation``</p ...
anonymous
core +1 more source

A Framework for Evaluating the Retrieval Effectiveness of Search Engines [PDF]

, 2012
This chapter presents a theoretical framework for evaluating next generation search engines. We focuson search engines whose results presentation is enriched with additional information and does notmerely present the usual list of “10 blue links”, that ...
Lewandowski, Dirk
core +1 more source

Retrieval-augmented AI assistants for healthcare: System design and evaluation [PDF]

This dissertation explores the application of Retrieval-Augmented Generation (RAG) in AI models to address the challenges posed by fragmented personal health data.
Meleka, Mark
core +1 more source

Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review

Mathematics
Retrieval-augmented generation (RAG) leverages the strengths of information retrieval and generative models to enhance the handling of real-time and domain-specific knowledge.
Wan Zhang, Jing Zhang
doaj +1 more source

Tracert-Retrieval-Augmented Generation: Boosting Multi-Hop Retrieval-Augmented Generation with Direction-Aware Graph Traversal

Engineering Proceedings
Tracert-retrieval-augmented generation (RAG) is a novel retrieval-augmented framework designed for efficient, document-level multi-hop reasoning. Unlike conventional RAG systems that retrieve top-k text segments based solely on dense similarity, Tracert ...
Siu-Him Zhang, Jhe-Wei Lin
doaj +1 more source

Next Sentence Prediction with BERT as a Dynamic Chunking Mechanism for Retrieval-Augmented Generation Systems

Proceedings of the International Florida Artificial Intelligence Research Society Conference
Retrieval-Augmented Generation systems enhance the generative capabilities of large language models by grounding their responses in external knowledge bases, addressing some of their major limitations and improving their reliability for tasks requiring ...
Alexandre Thurow Bender +3 more
doaj +1 more source

Artificial intelligence-driven clinical guideline recommendations in maternal care: How trustworthy are they?

Biomédica: revista del Instituto Nacional de Salud
Introduction. Medical staff often face difficulties in consulting and applying clinical guidelines in practice. Large language models, especially when combined with retrieval-augmented generation, may help overcome these challenges by producing context ...
Jairo J. Pérez +9 more
doaj +1 more source

Review of Document Q&A Driven by Multimodal Retrieval-Augmented Generation (Invited) [PDF]

Jisuanji gongcheng
Traditional Retrieval-Augmented Generation (RAG) methods predominantly focus on pure-text scenarios. In these scenarios, their retrieval and generation mechanisms encounter difficulties in effectively modeling common visual elements, spatial layouts, and
LI Zeming, WANG Shuliang, SHANG Zihe, SHENG Ming
doaj +1 more source

RAGdeterm: Deterministic retrieval-augmented generation for code generation

SoftwareX
Large language models (LLMs) are increasingly used in software development, yet effective code generation requires reliable access to up-to-date project-specific source code. This paper introduces RAGdeterm, a deterministic Retrieval-Augmented Generation
A. Bochenek, J. Protasiewicz, W. Pedrycz
doaj +1 more source

A retrieval-augmented large language model for agricultural advisory on crop varieties and cultivation techniques

CTU Journal of Innovation and Sustainable Development
This study presents the design and implementation of an agricultural advisory chatbot to help farmers access reliable information on crop varieties and cultivation techniques, with a focus on rice and mango.
Thanh Dien Tran +5 more
doaj +1 more source

retrieval-augmented generation
large language models
rag

artificial intelligence
llm
retrieval-augmented generation rag

information retrieval