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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 1276–1300 of 2111 papers

TitleStatusHype
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement—0
C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System—0
What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context—0
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation—0
A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models—0
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&ACode0
Advanced ingestion process powered by LLM parsing for RAG system—0
Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm—0
Unanswerability Evaluation for Retrieval Augmented Generation—0
LogBabylon: A Unified Framework for Cross-Log File Integration and Analysis—0
Attention with Dependency Parsing Augmentation for Fine-Grained Attribution—0
Let your LLM generate a few tokens and you will reduce the need for retrieval—0
One-Shot Multilingual Font Generation Via ViT—0
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models—0
Streamlining Systematic Reviews: A Novel Application of Large Language Models—0
VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation—0
Inference Scaling for Bridging Retrieval and Augmented Generation—0
Accelerating Retrieval-Augmented Generation—0
RAGServe: Fast Quality-Aware RAG Systems with Configuration Adaptation—0
Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous Data with RAG Systems—0
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation—0
Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge Graph-Based RAG—0
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models—0
Assessing the Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies—0
Federated In-Context LLM Agent Learning—0
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