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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
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources—0
CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval—0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination—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
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
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts—0
Agentic Retrieval-Augmented Generation for Time Series Analysis—0
Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild—0
Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM—0
From "Hallucination" to "Suture": Insights from Language Philosophy to Enhance Large Language Models—0
Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks—0
From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants—0
Improving Embedding Accuracy for Document Retrieval Using Entity Relationship Maps and Model-Aware Contrastive Sampling—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
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring—0
From Code Generation to Software Testing: AI Copilot with Context-Based RAG—0
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents—0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
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