RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
arXiv:2607.06527v1 Announce Type: cross Abstract: Multi-hop Question Answering over Knowledge Graphs faces a critical challenge: traditional retrieve-then-read pipelines break differentiability, preventing the retriever from learning to bridge the semantic gap where intermediate nodes lack lexical overlap with the query. To address this, we propose RSF-GLLM, a framework decoupling differentiable graph reasoning from answer generation. Our Recurrent Soft-Flow (RSF) module employs a GRU-guided query updater to propagate continuous relevance scores, utilizing a dynamic gating mechanism to travers
The continuous evolution of large language models and graph neural networks is enabling new approaches to complex knowledge representation and reasoning, addressing previous limitations in multi-hop QA.
This research directly tackles a significant bottleneck in complex AI reasoning over structured data, improving the accuracy and explainability of systems that rely on knowledge graphs.
The ability of AI systems to perform multi-hop reasoning over knowledge graphs is enhanced, reducing the 'semantic gap' and leading to more robust and reliable question-answering capabilities.
- · AI researchers
- · Knowledge graph developers
- · Complex QA system providers
- · Enterprises with rich internal data
- · Current retrieval-then-read only systems
More accurate and efficient retrieval and reasoning over large-scale knowledge graphs.
Improved performance of AI agents and decision-making systems that rely on structured information.
Accelerated development of domain-specific AI assistants capable of nuanced, multi-step inference.
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Read at arXiv cs.AI