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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 1651–1700 of 2111 papers

TitleStatusHype
Context-Augmented Code Generation Using Programming Knowledge Graphs—0
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge Graph-Based RAG—0
Context Embeddings for Efficient Answer Generation in RAG—0
Context Tuning for Retrieval Augmented Generation—0
Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems—0
Continually Self-Improving Language Models for Bariatric Surgery Question--Answering—0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation—0
Controlled Retrieval-augmented Context Evaluation for Long-form RAG—0
ControlNET: A Firewall for RAG-based LLM System—0
Control Token with Dense Passage Retrieval—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems—0
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
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