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RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation

Paper proposing a method for multi-hop knowledge graph question answering using recurrent soft-flow and decoupled large language model generation.

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RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation

arXiv
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The paper introduces RSF-GLLM, a framework that addresses the semantic gap in multi-hop knowledge graph question answering. It combines recurrent soft-flow with decoupled large language model generation to improve performance.

The method is evaluated on several benchmarks and shows promising results.

Abstract

The paper introduces RSF-GLLM, a framework that addresses the semantic gap in multi-hop knowledge graph question answering. It combines recurrent soft-flow with decoupled large language model generation to improve performance. The method is evaluated on several benchmarks and shows promising results.

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multi-hop qaknowledge graph question answeringrecurrent soft-flowdecoupled llm generationKnowledge GraphsLarge Language ModelsSemantic Interoperability
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