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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 1251–1275 of 2111 papers

TitleStatusHype
Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning—0
Differential Privacy of Cross-Attention with Provable Guarantee—0
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform—0
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation—0
Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency—0
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering—0
Diversity Enhances an LLM's Performance in RAG and Long-context Task—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
DMQR-RAG: Diverse Multi-Query Rewriting for RAG—0
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers—0
DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients—0
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
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