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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 1326–1350 of 2111 papers

TitleStatusHype
Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation—0
Semantic Tokens in Retrieval Augmented Generation—0
CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks—0
Leveraging Large Language Models to Democratize Access to Costly Datasets for Academic Research—0
Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset—0
Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking—0
Query Performance Explanation through Large Language Model for HTAP Systems—0
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring—0
Rethinking Strategic Mechanism Design In The Age Of Large Language Models: New Directions For Communication Systems—0
Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation—0
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems—0
Generating a Low-code Complete Workflow via Task Decomposition and RAG—0
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation—0
Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation—0
Towards Understanding Retrieval Accuracy and Prompt Quality in RAG Systems—0
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs—0
Knowledge Management for Automobile Failure Analysis Using Graph RAG—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis—0
Efficient Learning Content Retrieval with Knowledge Injection—0
Habit Coach: Customising RAG-based chatbots to support behavior change—0
Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMsCode0
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation—0
Human-Calibrated Automated Testing and Validation of Generative Language Models—0
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