Results 1 to 10 of about 3,238,639 (146)

Nursing Retrieval-Augmented Generation: Retrieval augmented generation for nursing question answering with large language models [PDF]

International Journal of Nursing Sciences
Objective: This study aimed to develop a Nursing Retrieval-Augmented Generation (NurRAG) system based on large language models (LLMs) and to evaluate its accuracy and clinical applicability in nursing question answering. Methods: A multidisciplinary team
Liping Xiong, Qiqiao Zeng, Weixiang Luo, Ronghui Liu   +3 more
doaj   +4 more sources

Hippocampo-neocortical interaction as compressive retrieval-augmented generation [PDF]

Nature Communications
Many aspects of learning, memory, and problem solving involve interplay between episodic (hippocampal) and semantic (neocortical) systems, but the neural mechanisms supporting this are unclear.
Eleanor Spens, Neil Burgess
doaj   +2 more sources

DC-RAG: a dual-channel retrieval-augmented generation framework for audit analysis [PDF]

Scientific Reports
With the continuous growth of information retrieval and knowledge acquisition demands, intelligent question-answering systems have been widely adopted across various vertical domains.
Chunyu Xing, Hang Meng
doaj   +2 more sources

Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems

Applied Sciences
A conversational system is an artificial intelligence application designed to interact with users in natural language, providing accurate and contextually relevant responses.
Edmund V. Ndimbo, Qin Luo, Gimo C. Fernando, Xu Yang, Bang Wang   +4 more
doaj   +2 more sources

Hierarchical context enhancement for long-tail entity retrieval augmented generation [PDF]

Frontiers in Artificial Intelligence
IntroductionRetrieval-Augmented Generation (RAG) in Domain-specific Question Answering (DSQA) often faces significant performance degradation due to semantic drift.
Yixuan Peng, Kewu Pan
doaj   +2 more sources

Retrieval augmented generation in dentistry: potentials, applications, and future directions. [PDF]

Front Dent Med
Firoozi T, Adabdokht R, Lai H.
europepmc   +2 more sources

Active Retrieval Augmented Generation [PDF]

, 2023
Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output.
Gao, Luyu, Liu, Qian, Neubig, Graham, Jiang, Zhengbao, Xu, Frank F., Sun, Zhiqing, Dwivedi-Yu, Jane, Yang, Yiming, Callan, Jamie   +8 more
core   +1 more source

Survey on Retrieval-Augmented Generation for Task Planning [PDF]

Jisuanji kexue yu tansuo
Retrieval-augmented generation (RAG) technology has become a key paradigm for improving the accuracy of large language model task response by dynamically integrating external knowledge to effectively alleviate the hallucination problem and knowledge ...
MA Yibo, CHEN Xiliang, ZHANG Legui, LAI Jun
doaj   +1 more source

Exploring Retrieval Augmented Generation in Arabic

Procedia Computer Science
Recently, Retrieval Augmented Generation (RAG) has emerged as a powerful technique in natural language processing, combining the strengths of retrieval-based and generation-based models to enhance text generation tasks. However, the application of RAG in
El-Beltagy, Samhaa R., Abdallah, Mohamed A.   +1 more
exaly   +1 more source

DF-RAG:A Retrieval-augmented Generation Method Based on Query Rewriting and Knowledge Selection [PDF]

Jisuanji kexue
Large language models have demonstrated formidable comprehension abilities in conversational tasks,yet they still face issues such as data timeliness and inefficiency in handling specific knowledge.To address these challenges,Retrieval-augmented ...
ZHANG Haoran, HAO Wenning, JIN Dawei, CHENG Kai, ZHAI Ying
doaj   +1 more source