Results 21 to 30 of about 14,626 (158)

Retrieval Augmented Recipe Generation

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
ACCEPT on IEEE/CVF Winter Conference on Applications of Computer Vision (WACV ...
LIU, Guoshan, YIN, Hailong, ZHU, Bin, CHEN, Jingjing, NGO, Chong-wah, JIANG, Yu-Gang   +5 more
openaire   +2 more sources

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, Nguyen Phuc Thinh Tran, Duy Anh Le, Thai-Nghe Nguyen, Khoa Anh Phan, Huu Hoa Nguyen   +5 more
doaj   +1 more source

Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy

AI
Background: Both early researchers, such as new graduate students, and experienced researchers face the challenge of sifting through vast amounts of literature to find their needle in a haystack.
Joëd Ngangmeni, Danda B. Rawat
doaj   +1 more source

Neurosymbolic Retrievers for Retrieval-Augmented Generation

IEEE Intelligent Systems
Retrieval Augmented Generation (RAG) has made significant strides in overcoming key limitations of large language models, such as hallucination, lack of contextual grounding, and issues with transparency. However, traditional RAG systems consist of three interconnected neural components - the retriever, re-ranker, and generator - whose internal ...
Yash Saxena, Manas Gaur
openaire   +2 more sources

Dynamic and Parametric Retrieval-Augmented Generation

Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
Retrieval-Augmented Generation (RAG) has become a foundational paradigm for equipping large language models (LLMs) with external knowledge, playing a critical role in information retrieval and knowledge-intensive applications. However, conventional RAG systems typically adopt a static retrieve-then-generate pipeline and rely on in-context knowledge ...
Weihang Su, Qingyao Ai, Jingtao Zhan, Qian Dong, Yiqun Liu 0001   +4 more
openaire   +3 more sources

Implementing Retrieval-Augmented Generation for Academic Libraries

International Journal of Librarianship
This article details the technical development of a Retrieval-Augmented Generation (RAG) system designed to enhance discovery within an academic library's institutional repository.
Wei Xuan
doaj   +1 more source

Data Imputation Based on Retrieval-Augmented Generation

Applied Sciences
Modern organizations collect increasing volumes of data to drive decision-making, often stored in centralized repositories such as data lakes, which consist of diverse structured and unstructured datasets.
Xiaojun Shi, Jiacheng Wang, Gregorius Justin Chung, Derick Julian, Lianpeng Qiao   +4 more
doaj   +1 more source

Hybrid retrieval generation for structured reasoning with large language models

Discover Artificial Intelligence
Large Language Models (LLMs) exhibit strong generative capabilities but remain limited in structured knowledge domains due to factual inconsistency, shallow multi-hop reasoning, and weak alignment with domain constraints.
Rathinasamy Muthusami, Kandhasamy Saritha   +1 more
doaj   +1 more source

Improving negative rejection ability in language models: A review of fine-tuned LLMs, RAG, and RAFT

Journal of King Saud University: Computer and Information Sciences
Large Language Models (LLMs) excel in text understanding and generation but struggle to reject irrelevant, ambiguous, or misleading queries, termed negative rejection, impacting reliability in high-stakes contexts.
Li Bowen, Zhong Zhuoqing, Zhang Hengyu, Sun Xubin, Baha Ihnaini   +4 more
doaj   +1 more source