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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
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study—0
Superpixel-Based Building Damage Detection from Post-earthquake Imagery Using Deep Neural Networks—0
SuperRAG: Beyond RAG with Layout-Aware Graph Modeling—0
Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judges—0
SurgRAW: Multi-Agent Workflow with Chain-of-Thought Reasoning for Surgical Intelligence—0
Sustainable Digitalization of Business with Multi-Agent RAG and LLM—0
SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models—0
SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents—0
Swiss Parliaments Corpus Re-Imagined (SPC_R): Enhanced Transcription with RAG-based Correction and Predicted BLEU—0
SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration—0
SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning—0
Synergizing RAG and Reasoning: A Systematic Review—0
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages—0
Synthetic Interlocutors. Experiments with Generative AI to Prolong Ethnographic Encounters—0
System Prompt Poisoning: Persistent Attacks on Large Language Models Beyond User Injection—0
Tabular Embedding Model (TEM): Finetuning Embedding Models For Tabular RAG Applications—0
Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting—0
Talking like Piping and Instrumentation Diagrams (P&IDs)—0
Talking to GDELT Through Knowledge Graphs—0
Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering—0
Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation—0
Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification—0
T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent—0
Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specifications—0
Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routing—0
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