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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 1626–1650 of 2111 papers

TitleStatusHype
Bio-Eng-LMM AI Assist chatbot: A Comprehensive Tool for Research and EducationCode0
Knowing When to Ask -- Bridging Large Language Models and Data—0
Retrieval Augmented Correction of Named Entity Speech Recognition Errors—0
Column Vocabulary Association (CVA): semantic interpretation of dataless tables—0
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support—0
Vietnamese Legal Information Retrieval in Question-Answering System—0
RAG based Question-Answering for Contextual Response Prediction System—0
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding—0
MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering—0
GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models—0
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering—0
Creating a Gen-AI based Track and Trace Assistant MVP (SuperTracy) for PostNL—0
MoA is All You Need: Building LLM Research Team using Mixture of Agents—0
Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culture—0
In Defense of RAG in the Era of Long-Context Language Models—0
You Only Use Reactive Attention Slice For Long Context RetrievalCode0
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical StudyCode0
AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models—0
BEAVER: An Enterprise Benchmark for Text-to-SQL—0
Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&ACode0
The Design of an LLM-powered Unstructured Analytics System—0
A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine—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
OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography—0
RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance—0
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