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Constrained Entity Selection under Partial Knowledge for LLM-Based Knowledge Graph QA

Large language models are increasingly used for knowledge graph question answering (KGQA), but can fail to correctly ground answers in the underlying graph. Current approaches to LLM-based KGQA either rely on full semantic parsing into executable queries such as SPARQL, which is brittle in practice due to complex schemas or incompleteness of real-world KGs, or on LLM-reasoning and answer…

We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.

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