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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 851–875 of 2111 papers

TitleStatusHype
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
RAG-Reward: Optimizing RAG with Reward Modeling and RLHF—0
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home—0
Leveraging Large Language Models to Enhance Machine Learning Interpretability and Predictive Performance: A Case Study on Emergency Department Returns for Mental Health Patients—0
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language ModelsCode7
Med-R^2: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based MedicineCode1
ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented GenerationCode0
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class ImbalanceCode0
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation—0
Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP DocumentsCode1
Zep: A Temporal Knowledge Graph Architecture for Agent MemoryCode12
ImageRef-VL: Enabling Contextual Image Referencing in Vision-Language ModelsCode0
RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News ArticlesCode0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented GenerationCode7
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender SystemsCode0
InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language ModelsCode1
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—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
4bit-Quantization in Vector-Embedding for RAGCode0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
Passage Segmentation of Documents for Extractive Question Answering—0
AirRAG: Activating Intrinsic Reasoning for Retrieval Augmented Generation via Tree-based Search—0
Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems—0
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