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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 1726–1750 of 2111 papers

TitleStatusHype
Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation—0
Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments—0
Does RAG Really Perform Bad For Long-Context Processing?—0
Do Large Language Models Know Conflict? Investigating Parametric vs. Non-Parametric Knowledge of LLMs for Conflict Forecasting—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
Don't Forget to Connect! Improving RAG with Graph-based Reranking—0
Don't Lag, RAG: Training-Free Adversarial Detection Using RAG—0
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
Do RAG Systems Suffer From Positional Bias?—0
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation—0
Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases—0
Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications—0
Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM—0
DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering—0
DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation—0
DuetRAG: Collaborative Retrieval-Augmented Generation—0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems—0
Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation—0
DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs—0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models—0
E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness—0
ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction—0
EchoSight: Advancing Visual-Language Models with Wiki Knowledge—0
EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation—0
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