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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 1376–1400 of 2111 papers

TitleStatusHype
Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements—0
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential—0
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps—0
Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation—0
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design—0
Accelerating Retrieval-Augmented Generation—0
Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild—0
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks—0
A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data—0
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
A Comparative Study of PDF Parsing Tools Across Diverse Document Categories—0
A Comparison of LLM Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Case—0
A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting—0
A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement—0
A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions—0
ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models—0
AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models—0
ADAM-1: AI and Bioinformatics for Alzheimer's Detection and Microbiome-Clinical Data Integrations—0
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation—0
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge—0
Adaptive Plan-Execute Framework for Smart Contract Security Auditing—0
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home—0
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario—0
Ad Auctions for LLMs via Retrieval Augmented Generation—0
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