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Research questionHow can LLMs answer knowledge-intensive questions over large, unseen knowledge graphs without graph-specific tuning?LLMs may lack the context and factual coverage needed for knowledge-intensive questions. Graph-based retrievers often require dataset-specific tuning and can become difficult to scale or generalize to unseen graphs.
AI
Information Retrieval
Machine Learning
Natural Language Processing
Reasoning
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.KGFR: A Foundation Retriever for Generalized Knowledge Graph Question AnsweringThe source studies an LLM collaborating with a structured knowledge-graph retriever through iterative node-, edge-, and path-level interactions. Its reported approach uses LLM-generated relation descriptions, question-conditioned entity initialization, and selective graph expansion; the abstract reports experiments indicating strong performance, scalability, and generalization, but does not specify the evaluated datasets or deployment access requirements.research paper · Sep 1, 2026
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