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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 901–925 of 2111 papers

TitleStatusHype
ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses—0
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QACode0
RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code GenerationCode0
An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata—0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question AnsweringCode0
FutureGen: LLM-RAG Approach to Generate the Future Work of Scientific ArticleCode0
Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG—0
Towards Lighter and Robust Evaluation for Retrieval Augmented GenerationCode0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection—0
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning—0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems—0
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation—0
Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual SettingsCode0
RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving—0
Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval—0
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation—0
From "Hallucination" to "Suture": Insights from Language Philosophy to Enhance Large Language Models—0
MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments—0
Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine—0
RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning—0
Generative AI for Software Architecture. Applications, Trends, Challenges, and Future Directions—0
Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval—0
OSCAR: Online Soft Compression And Reranking—0
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