arXiv:2410.06062
공식 코드 · Papers with Code
LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs
원문 초록
We introduce a Retrieval-Augmented Generation (RAG) system for translating user questions into accurate federated SPARQL queries over bioinformatics knowledge graphs (KGs) leveraging Large Language Models (LLMs). To enhance accuracy and reduce hallucinations in query generation, our system utilises metadata from the KGs, including query examples and schema information, and incorporates a validation step to correct generated queries. The system is available online at chat.expasy.org.
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Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning
EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree
토론
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