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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 1201–1250 of 2111 papers

TitleStatusHype
CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation—0
CoRAG: Collaborative Retrieval-Augmented Generation—0
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation—0
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG—0
Corpus-informed Retrieval Augmented Generation of Clarifying Questions—0
Correctness is not Faithfulness in RAG Attributions—0
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models—0
CPR: Retrieval Augmented Generation for Copyright Protection—0
Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines—0
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs—0
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models—0
Creating a Gen-AI based Track and Trace Assistant MVP (SuperTracy) for PostNL—0
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT—0
Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance—0
CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation—0
CtrlRAG: Black-box Adversarial Attacks Based on Masked Language Models in Retrieval-Augmented Language Generation—0
CUB: Benchmarking Context Utilisation Techniques for Language Models—0
CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model—0
Current state of LLM Risks and AI Guardrails—0
CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation—0
Cyber Knowledge Completion Using Large Language Models—0
CyberRAG: An agentic RAG cyber attack classification and reporting tool—0
DailyQA: A Benchmark to Evaluate Web Retrieval Augmented LLMs Based on Capturing Real-World Changes—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors—0
DataMosaic: Explainable and Verifiable Multi-Modal Data Analytics through Extract-Reason-Verify—0
Data Science Students Perspectives on Learning Analytics: An Application of Human-Led and LLM Content Analysis—0
Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs—0
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation—0
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction—0
Decentralizing AI Memory: SHIMI, a Semantic Hierarchical Memory Index for Scalable Agent Reasoning—0
Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation—0
Decoding the Flow: CauseMotion for Emotional Causality Analysis in Long-form Conversations—0
DECO: Life-Cycle Management of Enterprise-Grade Copilots—0
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models—0
DeepThink: Aligning Language Models with Domain-Specific User Intents—0
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination—0
Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation—0
Developing an Artificial Intelligence Tool for Personalized Breast Cancer Treatment Plans based on the NCCN Guidelines—0
Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems—0
Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties—0
Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report—0
Development of a Reliable and Accessible Caregiving Language Model (CaLM)—0
Dewey Long Context Embedding Model: A Technical Report—0
DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databases—0
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems—0
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue—0
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking—0
Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs—0
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs—0
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