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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 476–500 of 2111 papers

TitleStatusHype
Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph—0
PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood EstimationCode0
From Code Generation to Software Testing: AI Copilot with Context-Based RAG—0
CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation—0
WikiVideo: Article Generation from Multiple VideosCode1
Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation—0
AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening—0
Command A: An Enterprise-Ready Large Language Model—0
ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations—0
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems—0
Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework—0
Medical large language models are easily distractedCode0
LLM-Assisted Proactive Threat Intelligence for Automated Reasoning—0
Beyond Quacking: Deep Integration of Language Models and RAG into DuckDBCode3
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented GenerationCode9
InteractiveSurvey: An LLM-based Personalized and Interactive Survey Paper Generation SystemCode1
CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation—0
Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation—0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—0
Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge EnhancementCode2
Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation—0
SCORE: Story Coherence and Retrieval Enhancement for AI Narratives—0
SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization—0
GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement—0
MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation—0
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