Results 1 to 10 of about 3,227,651 (213)

Fine grained reranking via caption bridging for knowledge augmented visual question answering [PDF]

Scientific Reports
Retrieval-Augmented Generation (RAG) has emerged as a pivotal framework for knowledge-intensive reasoning by coupling external retrieval with generative capabilities.
JunZhe Feng, Tao Liu, Yuhang Wu, Zi-Neng Ng, Mohammadmahdi Ariannejad, Jian Ding Tan, Mohammad Arif Sobhan Bhuiyan, Chia Chao Kang   +7 more
doaj   +2 more sources

Low-energy small language models with retrieval-augmented generation can surpass large-model performance in rheumatology [PDF]

Frontiers in Medicine
BackgroundLarge language models (LLMs) are increasingly explored for clinical decision support but are limited by high computational and energy demands. Smaller language models (SLMs), particularly when combined with retrieval-augmented generation (RAG),
Sabine Felde, Rüdiger Buchkremer, Gamal Chehab, Gamal Chehab, Gamal Chehab, Christian Thielscher, Jörg H. W. Distler, Jörg H. W. Distler, Matthias Schneider, Matthias Schneider, Jutta G. Richter, Jutta G. Richter   +11 more
doaj   +2 more sources

Enhancing the Precision and Interpretability of Retrieval-Augmented Generation (RAG) in Legal Technology: A Survey

IEEE Access
Retrieval-Augmented Generation (RAG) is a promising solution that can enhance the capabilities of large language model (LLM) applications in critical domains, including legal technology, by retrieving knowledge from external databases.
Mahd Hindi, Linda Mohammed, Ommama Maaz, Abdulmalik Alwarafy   +3 more
doaj   +3 more sources

ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG)

IEEE Access
Document processing and query generation tasks in customs declaration scenarios face key challenges such as the complexity of multimodal data, adaptability to dynamic regulations, and ambiguity in query semantics.
Rong Hu, Sen Liu, Panpan Qi, Jingyi Liu, Fengyuan Li   +4 more
doaj   +3 more sources

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

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

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

Resource-Constrained Evaluation of Quantized Local LLMs for Retrieval-Augmented Generation on a Reduced-Corpus Setting

IEEE Access
This paper studies retrieval-augmented generation (RAG) under a realistic local deployment constraint. Rather than proposing a new retriever or generator architecture, the paper evaluates how local, quantized RAG behaves when answer quality, provenance ...
Marcio L. Lima de Oliveira, Jes de Jesus Fiais Cerqueira   +1 more
doaj   +1 more source

Using Retrieval vs. Cache Augmented Generation for a Pok´emon Chatbot

Proceedings of the International Florida Artificial Intelligence Research Society Conference
Cache Augmented Generation (CAG) can be an alternative to Retrieval Augmented Generation (RAG). There are differences between the two frameworks, but they work largely in the same way.
Cengiz Gunay, Jonathan Tran
doaj   +1 more source

Meta-RAG: A Metadata-Driven Retrieval-Augmented Generation Framework for the Power Industry [PDF]

Jisuanji gongcheng
Large Language Models (LLMs) have made significant progress in dialogue, reasoning, and knowledge retention. However, they still face challenges in terms of factual accuracy, knowledge updates, and a lack of high-quality domain datasets for handling ...
WANG Heqing, WEI Jie, JING Hongyu, SONG Hui, XU Bo
doaj   +1 more source