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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 1501–1525 of 2111 papers

TitleStatusHype
CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-CheckingCode0
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering—0
Extracting polygonal footprints in off-nadir images with Segment Anything ModelCode1
Plan with Code: Comparing approaches for robust NL to DSL generation—0
Graph Retrieval-Augmented Generation: A SurveyCode3
W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question AnsweringCode1
Retail-GPT: leveraging Retrieval Augmented Generation (RAG) for building E-commerce Chat AssistantsCode1
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented GenerationCode5
Exploring Retrieval Augmented Generation in ArabicCode0
WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs—0
OpenResearcher: Unleashing AI for Accelerated Scientific ResearchCode3
Bayesian inference to improve quality of Retrieval Augmented Generation—0
A New Pipeline For Generating Instruction Dataset via RAG and Self Fine-Tuning—0
Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models—0
Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—0
Retrieval-augmented code completion for local projects using large language models—0
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction—0
A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning—0
ConfusedPilot: Confused Deputy Risks in RAG-based LLMs—0
Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction—0
Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented GenerationCode4
Towards Explainable Network Intrusion Detection using Large Language Models—0
EfficientRAG: Efficient Retriever for Multi-Hop Question AnsweringCode2
ACL Ready: RAG Based Assistant for the ACL ChecklistCode0
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