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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 2076–2100 of 2111 papers

TitleStatusHype
Knowledge Synthesis of Photosynthesis Research Using a Large Language Model—0
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems—0
KodeXv0.1: A Family of State-of-the-Art Financial Large Language Models—0
KRAG Framework for Enhancing LLMs in the Legal Domain—0
KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models—0
Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine—0
Language Model Re-rankers are Steered by Lexical Similarities—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Language Models are Few-Shot Graders—0
Language Models "Grok" to Copy—0
Language verY Rare for All—0
LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation—0
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning—0
Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization—0
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems—0
LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation—0
LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India—0
LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation—0
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models—0
Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
Learning variant product relationship and variation attributes from e-commerce website structures—0
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA—0
LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval—0
LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration—0
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