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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 1026–1050 of 2111 papers

TitleStatusHype
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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