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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 1001–1050 of 2111 papers

TitleStatusHype
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs—0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification—0
Improving Zero-shot LLM Re-Ranker with Risk Minimization—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—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
In-depth Analysis of Graph-based RAG in a Unified Framework—0
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization—0
Inference Scaling for Bridging Retrieval and Augmented Generation—0
Inference Scaling for Long-Context Retrieval Augmented Generation—0
Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing—0
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation—0
CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning—0
InfoTech Assistant : A Multimodal Conversational Agent for InfoTechnology Web Portal Queries—0
Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG—0
Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security—0
In-Place Updates of a Graph Index for Streaming Approximate Nearest Neighbor Search—0
Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud—0
Formal Language Knowledge Corpus for Retrieval Augmented Generation—0
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering—0
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
Flooding edge or node weighted graphs—0
Flippi: End To End GenAI Assistant for E-Commerce—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework—0
Accelerating Retrieval-Augmented Generation—0
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research—0
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity—0
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—0
FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning—0
First Token Probability Guided RAG for Telecom Question Answering—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
Claim Verification in the Age of Large Language Models: A Survey—0
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences—0
FinTextQA: A Dataset for Long-form Financial Question Answering—0
FinSage: A Multi-aspect RAG System for Financial Filings Question Answering—0
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain—0
A review of faithfulness metrics for hallucination assessment in Large Language Models—0
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs—0
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations—0
Fine-tuning Large Language Models for Domain-specific Machine Translation—0
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