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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 1401–1450 of 2111 papers

TitleStatusHype
A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based InsightsCode0
Familiarity-Aware Evidence Compression for Retrieval-Augmented GenerationCode1
Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models—0
Retrieval-Augmented Test Generation: How Far Are We?—0
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented GenerationCode3
RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented DialoguesCode0
Should RAG Chatbots Forget Unimportant Conversations? Exploring Importance and Forgetting with Psychological InsightsCode0
VERA: Validation and Enhancement for Retrieval Augmented systems—0
RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models—0
Learning variant product relationship and variation attributes from e-commerce website structures—0
Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented GenerationCode1
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to RefuseCode2
SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer—0
P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task—0
Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style—0
THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language ModelsCode0
LLMs & XAI for Water Sustainability: Seasonal Water Quality Prediction with LIME Explainable AI and a RAG-based Chatbot for Insights—0
Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine—0
SFR-RAG: Towards Contextually Faithful LLMs—0
Trustworthiness in Retrieval-Augmented Generation Systems: A SurveyCode1
Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Block-Attention for Efficient RAGCode1
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language ModelsCode2
Language Models "Grok" to Copy—0
Hacking, The Lazy Way: LLM Augmented Pentesting—0
Winning Solution For Meta KDD Cup' 24—0
KodeXv0.1: A Family of State-of-the-Art Financial Large Language Models—0
LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation—0
A RAG Approach for Generating Competency Questions in Ontology Engineering—0
Exploring Information Retrieval Landscapes: An Investigation of a Novel Evaluation Techniques and Comparative Document Splitting MethodsCode0
Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift GeneralizationCode0
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains—0
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG—0
OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering—0
Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using JailbreakingCode0
Bio-Eng-LMM AI Assist chatbot: A Comprehensive Tool for Research and EducationCode0
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented GenerationCode9
Knowing When to Ask -- Bridging Large Language Models and Data—0
GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question AnsweringCode1
Revisiting the Solution of Meta KDD Cup 2024: CRAGCode2
Retrieval Augmented Correction of Named Entity Speech Recognition Errors—0
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge DiscoveryCode7
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMsCode2
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support—0
Column Vocabulary Association (CVA): semantic interpretation of dataless tables—0
Vietnamese Legal Information Retrieval in Question-Answering System—0
Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and EvaluationCode1
RAG based Question-Answering for Contextual Response Prediction System—0
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding—0
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