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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 1401–1425 of 2111 papers

TitleStatusHype
A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based InsightsCode0
Familiarity-Aware Evidence Compression for Retrieval-Augmented GenerationCode1
Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models—0
Retrieval-Augmented Test Generation: How Far Are We?—0
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented GenerationCode3
RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented DialoguesCode0
Should RAG Chatbots Forget Unimportant Conversations? Exploring Importance and Forgetting with Psychological InsightsCode0
VERA: Validation and Enhancement for Retrieval Augmented systems—0
RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models—0
Learning variant product relationship and variation attributes from e-commerce website structures—0
Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented GenerationCode1
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to RefuseCode2
SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer—0
P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task—0
Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style—0
THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language ModelsCode0
LLMs & XAI for Water Sustainability: Seasonal Water Quality Prediction with LIME Explainable AI and a RAG-based Chatbot for Insights—0
Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine—0
SFR-RAG: Towards Contextually Faithful LLMs—0
Trustworthiness in Retrieval-Augmented Generation Systems: A SurveyCode1
Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Block-Attention for Efficient RAGCode1
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language ModelsCode2
Language Models "Grok" to Copy—0
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