aiqbench analysis
This paper identifies data citation as a critical, unsolved challenge for large language models, distinguishing it from existing document-level grounding techniques. The authors outline three specific research directions: attributing training data influence within model parameters, identifying datasets at inference time with proper granularity, and defining provenance for knowledge graph facts. Solving these issues requires cross-disciplinary collaboration between AI, databases, and information retrieval communities to ensure output verifiability and fair credit distribution.
Data Citation for Large Language Models: A Challenge
arXiv:2608.25663v1 Announce Type: cross Abstract: 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.
Abstract: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.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Databases (cs.DB); Digital Libraries (cs.DL)
arXiv:2608.25663 [cs.IR]
(or arXiv:2608.25663v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2608.25663 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 283 words
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