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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 1201–1225 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
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