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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 1276–1300 of 2111 papers

TitleStatusHype
DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation—0
DuetRAG: Collaborative Retrieval-Augmented Generation—0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems—0
Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation—0
DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs—0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models—0
E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness—0
ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction—0
EchoSight: Advancing Visual-Language Models with Wiki Knowledge—0
EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation—0
EdgeRAG: Online-Indexed RAG for Edge Devices—0
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-k—0
Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
Efficient Federated Search for Retrieval-Augmented Generation—0
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
Efficient Knowledge Feeding to Language Models: A Novel Integrated Encoder-Decoder Architecture—0
Efficient Learning Content Retrieval with Knowledge Injection—0
Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP—0
Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services—0
Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations—0
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models—0
Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation—0
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling—0
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