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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 976–1000 of 2111 papers

TitleStatusHype
A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science—0
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval—0
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation—0
Knowledge Injection via Prompt Distillation—0
Query pipeline optimization for cancer patient question answering systems—0
VISA: Retrieval Augmented Generation with Visual Source Attribution—0
Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic SettingsCode0
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability—0
DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs—0
PA-RAG: RAG Alignment via Multi-Perspective Preference OptimizationCode1
Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation—0
Context-DPO: Aligning Language Models for Context-FaithfulnessCode1
Language verY Rare for All—0
EvoWiki: Evaluating LLMs on Evolving Knowledge—0
RAG for Effective Supply Chain Security Questionnaire Automation—0
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs—0
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference AlignmentCode1
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models—0
Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG IntegrationCode0
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models—0
A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models—0
SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented GenerationCode2
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation—0
What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context—0
OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial DomainCode2
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