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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 1001–1025 of 2111 papers

TitleStatusHype
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs—0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification—0
Improving Zero-shot LLM Re-Ranker with Risk Minimization—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
From Code Generation to Software Testing: AI Copilot with Context-Based RAG—0
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents—0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
In-depth Analysis of Graph-based RAG in a Unified Framework—0
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization—0
Inference Scaling for Bridging Retrieval and Augmented Generation—0
Inference Scaling for Long-Context Retrieval Augmented Generation—0
Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing—0
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation—0
CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning—0
InfoTech Assistant : A Multimodal Conversational Agent for InfoTechnology Web Portal Queries—0
Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG—0
Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security—0
In-Place Updates of a Graph Index for Streaming Approximate Nearest Neighbor Search—0
Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud—0
Formal Language Knowledge Corpus for Retrieval Augmented Generation—0
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering—0
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
Flooding edge or node weighted graphs—0
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