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It is argued that data citation for large language models is an open challenge, distinct from document-level citation grounding and harder to solve.
This study provides an initial assessment on the feasibility of using LLMs as a KB for CH, using “Galois”, a recent framework for executing Structured Query Language (SQL) queries over LLMs with logical and physical optimizations tailored to the model’s behavior.
Querying LLMs as digital libraries is feasible, but its effectiveness depends on model strength, deployment conditions, dataset structure, and execution strategy, and Galois remains valuable when relational discipline and controlled query execution are required.
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