Results 31 to 40 of about 3,238,639 (146)

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

Bridging the Question–Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings

IEEE Access
Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of responses. However, bridging the style gap between user queries and relevant information in document text
Domen Vake, Jernej Vicic, Aleksandar Tosic   +2 more
doaj   +1 more source

Multimodal retrieval-augmented generation framework for visually rich knowledge in the architecture domain

Architectural Intelligence
Architectural design relies heavily on rich and multimodal knowledge—including text descriptions, detailed tables, and complex visual information—to inform creative and technical decision-making.
Xianchuan Meng, Ziyu Tong
doaj   +1 more source

LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering

SoftwareX
Large language model (LLM) based interactive chatbots have been gaining popularity as a tool to serve organizational information among people. Building such a tool goes through several development phases i.e.
Md. Shahidul Salim, Sk Imran Hossain, Tanim Jalal, Dhiman Kumer Bose, Mohammad Jahid Ibna Basher   +4 more
doaj   +1 more source

Dynamic Retrieval-Augmented Generation [PDF]

Current state-of-the-art large language models are effective in generating high-quality text and encapsulating a broad spectrum of world knowledge. These models, however, often hallucinate and lack locally relevant factual data.
Litvinov, Denis, Galimzyanov, Timur, Zharov, Yaroslav, Shapkin, Anton, Bogomolov, Egor, Bryksin, Timofey   +5 more
core   +1 more source

Hallucination Reduction in Retrieval-Augmented Generation (RAG)

Research Status: Ongoing (Work in Progress) This research project focuses on developing novel methodologies to reduce hallucinations in Retrieval-Augmented Generation (RAG) systems using Large Language Models (LLMs).
Jahan Zaib
core   +6 more sources

Research on risk decision-making generation method for water conservancy project based on multimodal knowledge graph and large language model.

PLoS ONE
Traditional knowledge graphs of water conservancy project risks have supported risk decision-making. However, they are constrained by limited data modalities and low accuracy in information extraction.
Libo Yang, Yuan Li, Junhua Tan, Libo Mao
doaj   +1 more source

DMAR: Dynamic Multi-Anchor Retrieval with Structure-Aware Query Reformulation for Knowledge-Augmented Generation

Applied Sciences
Retrieval-Augmented Generation (RAG) has become an important paradigm for knowledge-intensive natural language processing, as it enables Large Language Models (LLMs) to access external evidence beyond their parametric memory.
Zhou Lei, Yanqi Xu, Shengbo Chen
doaj   +1 more source

Adaptive Multimodal Fusion Architecture for Heterogeneous Embedding Spaces in Retrieval-Augmented Generation Systems

IEEE Access
Retrieval-augmented generation systems integrate external information to mitigate hallucinations in large language models, yet existing multimodal retrieval-augmented generation implementations struggle with heterogeneous embedding spaces from diverse ...
Timothy Dillan, Sani Muhamad Isa, Abba Suganda Girsang, Derwin Suhartono   +3 more
doaj   +1 more source

Bridging text and topology: empowering large language models through graph retrieval for constraint-aware reasoning in campus digital twins

International Journal of Digital Earth
Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating human language from heterogeneous data sources.
Wenyu Zhang, Suphanut Jamonnak, Weishan Bai, Xinyue Ye   +3 more
doaj   +1 more source