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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 1176–1200 of 2111 papers

TitleStatusHype
SiReRAG: Indexing Similar and Related Information for Multihop Reasoning—0
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval—0
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context—0
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs—0
SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering—0
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model—0
Small Models, Big Support: A Local LLM Framework for Teacher-Centric Content Creation and Assessment using RAG and CAG—0
SmartLLM: Smart Contract Auditing using Custom Generative AI—0
SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback—0
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques—0
Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions—0
Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA)—0
Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting—0
Speech Retrieval-Augmented Generation without Automatic Speech Recognition—0
SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information—0
SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph—0
STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack—0
100% Elimination of Hallucinations on RAGTruth for GPT-4 and GPT-3.5 Turbo—0
SteLLA: A Structured Grading System Using LLMs with RAG—0
Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization—0
StreamingRAG: Real-time Contextual Retrieval and Generation Framework—0
Streamlining Systematic Reviews: A Novel Application of Large Language Models—0
Sufficient Context: A New Lens on Retrieval Augmented Generation Systems—0
SUGAR: Leveraging Contextual Confidence for Smarter Retrieval—0
SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer—0
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