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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 1351–1400 of 2111 papers

TitleStatusHype
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness—0
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need—0
RealDrive: Retrieval-Augmented Driving with Diffusion Models—0
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark—0
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—0
Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?—0
Real-time Spatial Retrieval Augmented Generation for Urban Environments—0
REAPER: Reasoning based Retrieval Planning for Complex RAG Systems—0
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
Reasoning LLMs for User-Aware Multimodal Conversational Agents—0
Attribution in Scientific Literature: New Benchmark and Methods—0
Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research—0
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability—0
Reducing hallucination in structured outputs via Retrieval-Augmented Generation—0
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models—0
Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach—0
Re-identification of De-identified Documents with Autoregressive Infilling—0
Reinforcement Learning for Optimizing RAG for Domain Chatbots—0
REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing—0
Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks—0
Remote Diffusion—0
RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service—0
Repoformer: Selective Retrieval for Repository-Level Code Completion—0
Re-ranking the Context for Multimodal Retrieval Augmented Generation—0
Reranking with Compressed Document Representation—0
Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning—0
ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations—0
Responsible Retrieval Augmented Generation for Climate Decision Making from Documents—0
Rethinking Strategic Mechanism Design In The Age Of Large Language Models: New Directions For Communication Systems—0
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge—0
Retrieval-Augmented Audio Deepfake Detection—0
Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models—0
Retrieval-augmented code completion for local projects using large language models—0
Retrieval Augmented Correction of Named Entity Speech Recognition Errors—0
Retrieval Augmented End-to-End Spoken Dialog Models—0
Retrieval-augmented Generation across Heterogeneous Knowledge—0
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support—0
Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps—0
Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning—0
Retrieval Augmented Generation Evaluation for Health Documents—0
Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU—0
Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine—0
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model—0
Retrieval-Augmented Generation for Natural Language Processing: A Survey—0
Retrieval-Augmented Generation for Service Discovery: Chunking Strategies and Benchmarking—0
Retrieval Augmented Generation for Topic Modeling in Organizational Research: An Introduction with Empirical Demonstration—0
Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications—0
Retrieval Augmented Generation Integrated Large Language Models in Smart Contract Vulnerability Detection—0
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