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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 851–875 of 2111 papers

TitleStatusHype
EnronQA: Towards Personalized RAG over Private Documents—0
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation—0
GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs—0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—0
GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Models—0
GEM: Empowering LLM for both Embedding Generation and Language Understanding—0
Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval On English Queries and Sanskrit Documents—0
GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems—0
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning—0
Generating a Low-code Complete Workflow via Task Decomposition and RAG—0
Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana—0
Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models—0
Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Comparative Analysis of Retrieval Systems in the Real World—0
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models—0
Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models—0
Generative AI in the Construction Industry: A State-of-the-art Analysis—0
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation—0
Generative Information Retrieval Evaluation—0
Answering real-world clinical questions using large language model based systems—0
Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models—0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—0
GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?—0
HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings—0
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