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This paper highlights the critical challenge of data citation in large language models (LLMs), arguing that traditional citation methods are insufficient for the unique context of data used in model training and inference. The authors propose three distinct research directions to address this issue: establishing training data attribution, improving data citation at inference time, and defining citation for knowledge graph facts. By emphasizing the need for verifiable outputs, traceable provenance, and proper credit to data creators, the paper calls for interdisciplinary collaboration to tackle these complexities.
Data citation in large language models is not just a verification issue—it's a complex challenge that demands new frameworks for credit and provenance.
Large language models increasingly mediate access to information, and a growing body of work asks whether they cite the sources behind their outputs. That work treats citation as a verification device and applies it to textual documents. Scholarly citation serves two further functions, credit and provenance, and it applies to data as much as to text. This paper argues that data citation for large language models is an open challenge, distinct from document-level citation grounding and harder to solve. We ask how such models should cite data so that outputs stay verifiable, provenance stays traceable, and credit reaches data creators and curators. We set out three research directions. Training data attribution has to turn influence estimates into references for corpora absorbed into model parameters. Data citation at inference time has to identify datasets, subsets, and query results at the right granularity and fixity. Citing knowledge graph facts has to define what a reference to a single triple denotes and how credit propagates along provenance. Progress on all three depends on joint work across the database, information retrieval, knowledge representation, and artificial intelligence communities.