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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
Command A: An Enterprise-Ready Large Language Model—0
Comparative Analysis of Retrieval Systems in the Real World—0
Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks—0
Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework—0
Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation—0
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering—0
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation—0
Conan-embedding: General Text Embedding with More and Better Negative Samples—0
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models—0
ConfusedPilot: Confused Deputy Risks in RAG-based LLMs—0
CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models—0
Context-Augmented Code Generation Using Programming Knowledge Graphs—0
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge Graph-Based RAG—0
Context Embeddings for Efficient Answer Generation in RAG—0
Context Tuning for Retrieval Augmented Generation—0
Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems—0
Continually Self-Improving Language Models for Bariatric Surgery Question--Answering—0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation—0
Controlled Retrieval-augmented Context Evaluation for Long-form RAG—0
ControlNET: A Firewall for RAG-based LLM System—0
Control Token with Dense Passage Retrieval—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems—0
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