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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
Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction—0
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications—0
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation—0
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT—0
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System—0
Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation—0
HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse—0
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents—0
AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments—0
Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation—0
Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise—0
An LLM Agent for Automatic Geospatial Data Analysis—0
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation—0
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism—0
Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation—0
Improving Factuality with Explicit Working Memory—0
CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model—0
Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework—0
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring—0
Current state of LLM Risks and AI Guardrails—0
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation—0
Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation—0
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