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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 651–700 of 2111 papers

TitleStatusHype
Federated In-Context LLM Agent Learning—0
Chats-Grid: An Iterative Retrieval Q&A Optimization Scheme Leveraging Large Model and Retrieval Enhancement Generation in smart grid—0
ChatQA: Surpassing GPT-4 on Conversational QA and RAG—0
Are Large Language Models In-Context Graph Learners?—0
ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities—0
Chatmap : Large Language Model Interaction with Cartographic Data—0
A GEN AI Framework for Medical Note Generation—0
Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation—0
DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton—0
Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses—0
A Reasoning-Focused Legal Retrieval Benchmark—0
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG—0
FastRAG: Retrieval Augmented Generation for Semi-structured Data—0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models—0
Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Flippi: End To End GenAI Assistant for E-Commerce—0
Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory—0
Characterizing Network Structure of Anti-Trans Actors on TikTok—0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems—0
Chain-of-Retrieval Augmented Generation—0
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement—0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device—0
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks—0
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation—0
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs—0
ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges—0
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps—0
C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System—0
CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models—0
A RAG-based Question Answering System Proposal for Understanding Islam: MufassirQAS LLM—0
AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production—0
CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation—0
CC-RAG: Structured Multi-Hop Reasoning via Theme-Based Causal Graphs—0
A RAG-Based Institutional Assistant—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential—0
SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval—0
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation—0
A RAG Approach for Generating Competency Questions in Ontology Engineering—0
Adversarial Databases Improve Success in Retrieval-based Large Language Models—0
CaseGPT: a case reasoning framework based on language models and retrieval-augmented generation—0
ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation—0
BioRAG: A RAG-LLM Framework for Biological Question Reasoning—0
Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation—0
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation—0
Advancing Vietnamese Information Retrieval with Learning Objective and Benchmark—0
Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces—0
Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method—0
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