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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 1076–1100 of 2111 papers

TitleStatusHype
Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs—0
NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question AnsweringCode0
CiteCheck: Towards Accurate Citation Faithfulness DetectionCode0
Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA—0
Agentic Verification for Ambiguous Query Disambiguation—0
LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs - No Silver Bullet for LC or RAG RoutingCode0
Post-training an LLM for RAG? Train on Self-Generated Demonstrations—0
MIR-Bench: Can Your LLM Recognize Complicated Patterns via Many-Shot In-Context Reasoning?—0
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation—0
NeuroXVocal: Detection and Explanation of Alzheimer's Disease through Non-invasive Analysis of Picture-prompted Speech—0
Diversity Enhances an LLM's Performance in RAG and Long-context Task—0
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables—0
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation—0
Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs—0
KIMAs: A Configurable Knowledge Integrated Multi-Agent System—0
ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation—0
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAGCode0
From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework—0
Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal ReasoningCode0
AI-VERDE: A Gateway for Egalitarian Access to Large Language Model-Based Resources For Educational Institutions—0
Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection—0
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation—0
GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing UnitsCode0
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning—0
Enhancing Financial Time-Series Forecasting with Retrieval-Augmented Large Language Models—0
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