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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 926–950 of 2111 papers

TitleStatusHype
Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA—0
Agentic Verification for Ambiguous Query Disambiguation—0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—0
Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning—0
Integrating Knowledge Retrieval and Large Language Models for Clinical Report Correction—0
GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs—0
Column Vocabulary Association (CVA): semantic interpretation of dataless tables—0
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation—0
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation—0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval—0
HD-RAG: Retrieval-Augmented Generation for Hybrid Documents Containing Text and Hierarchical Tables—0
GARLIC: LLM-Guided Dynamic Progress Control with Hierarchical Weighted Graph for Long Document QA—0
CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections—0
HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings—0
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations—0
GAIA: A General AI Assistant for Intelligent Accelerator Operations—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems—0
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks—0
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG—0
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems—0
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights—0
From RAG to RICHES: Retrieval Interlaced with Sequence Generation—0
Cognitive-Aligned Document Selection for Retrieval-augmented Generation—0
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