Results 1 to 10 of about 14,626 (158)
Nursing Retrieval-Augmented Generation: Retrieval augmented generation for nursing question answering with large language models [PDF]
International Journal of Nursing SciencesObjective: 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 +3 more
doaj +2 more sources
Hierarchical context enhancement for long-tail entity retrieval augmented generation [PDF]
Frontiers in Artificial IntelligenceIntroductionRetrieval-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
Medical LLMs: Fine-Tuning vs. Retrieval-Augmented Generation [PDF]
BioengineeringLarge language models (LLMs) are trained on huge datasets, which allow them to answer questions from various domains. However, their expertise is confined to the data that they were trained on. In order to specialize LLMs in niche domains like healthcare,
Bhagyajit Pingua +6 more
doaj +2 more sources
Graph Retrieval-Augmented Generation: A Survey
ACM Transactions on Information SystemsRecently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an external knowledge base, RAG refines LLM outputs, effectively mitigating issues such as “hallucination,” lack of domain-specific knowledge ...
Siliang Tang +2 more
exaly +3 more sources
Retrieval-Augmented Generation (RAG)
Business and Information Systems EngineeringKlesel Michael
exaly +3 more sources
Active Retrieval Augmented Generation
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 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. Augmenting LMs by retrieving information from external knowledge resources is one promising solution.
Zhengbao Jiang +8 more
openaire +2 more sources
Retrieval Augmented Generation
Retrieval-augmented generation (RAG) is a hybrid architecture that combines the generative power of large language models (LLMs) with the factual reliability of information retrieval systems. Although the emergence of large language models (LLMs) has significantly improved the performance of natural language understanding and generation tasks. However,
Jingsong Shawn Yu, Yazhi Yao
+9 more sources
Retrieval Augmented Code Generation and Summarization [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2021, 2021 accepted in EMNLP-Findings ...
Md. Rizwan Parvez +4 more
openaire +2 more sources
DF-RAG:A Retrieval-augmented Generation Method Based on Query Rewriting and Knowledge Selection [PDF]
Jisuanji kexueLarge 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
Retrieval-Augmented Controllable Review Generation [PDF]
Proceedings of the 28th International Conference on Computational Linguistics, 2020 In this paper, we study review generation given a set of attribute identifiers which are user ID, product ID and rating. This is a difficult subtask of natural language generation since models are limited to the given identifiers, without any specific descriptive information regarding the inputs, when generating the text.
Jihyeok Kim +3 more
openaire +1 more source