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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 1151–1175 of 2111 papers

TitleStatusHype
StreamingRAG: Real-time Contextual Retrieval and Generation Framework—0
GraphRAG under Fire—0
A RAG-Based Institutional Assistant—0
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation—0
RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation—0
K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected CompressorCode0
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models—0
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home—0
Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana—0
RAG-Reward: Optimizing RAG with Reward Modeling and RLHF—0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
Data Science Students Perspectives on Learning Analytics: An Application of Human-Led and LLM Content Analysis—0
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class ImbalanceCode0
Leveraging Large Language Models to Enhance Machine Learning Interpretability and Predictive Performance: A Case Study on Emergency Department Returns for Mental Health Patients—0
ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented GenerationCode0
RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News ArticlesCode0
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender SystemsCode0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
ImageRef-VL: Enabling Contextual Image Referencing in Vision-Language ModelsCode0
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation—0
Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments—0
Visual RAG: Expanding MLLM visual knowledge without fine-tuning—0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
AirRAG: Activating Intrinsic Reasoning for Retrieval Augmented Generation via Tree-based Search—0
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