AF-Retriever delivers state-of-the-art zero- and one-shot results on three STaRK QA benchmarks by using LLM extraction, vector similarity, incremental scope expansion, and hybrid retrieval.
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RAS conditions each new Cypher query attempt on prior execution errors through ICL and reduces execution error rate by 41-50% at n=5 versus 32-38% for independent scaling across three Neo4j datasets and five models.
The report summarizes key research directions, challenges, and solutions from the LLM+Graph workshop at VLDB 2025.
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Autofocus Retrieval: An Effective Pipeline for Multi-Hop Question Answering With Semi-Structured Knowledge
AF-Retriever delivers state-of-the-art zero- and one-shot results on three STaRK QA benchmarks by using LLM extraction, vector similarity, incremental scope expansion, and hybrid retrieval.
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RAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation
RAS conditions each new Cypher query attempt on prior execution errors through ICL and reduces execution error rate by 41-50% at n=5 versus 32-38% for independent scaling across three Neo4j datasets and five models.
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LLM+Graph@VLDB'2025 Workshop Summary
The report summarizes key research directions, challenges, and solutions from the LLM+Graph workshop at VLDB 2025.