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Retrieval-augmented Generation

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Showing 926–950 of 2196 papers

TitleStatusHype
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity—0
Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security—0
Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm—0
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLM—0
Flippi: End To End GenAI Assistant for E-Commerce—0
Claim Verification in the Age of Large Language Models: A Survey—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
GLLM: Self-Corrective G-Code Generation using Large Language Models with User Feedback—0
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization—0
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research—0
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
First Token Probability Guided RAG for Telecom Question Answering—0
CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
FinTextQA: A Dataset for Long-form Financial Question Answering—0
FinSage: A Multi-aspect RAG System for Financial Filings Question Answering—0
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain—0
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search—0
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
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