arXiv:2605.20478v1 Announce Type: new Abstract: LLM-curated tables can appear source-grounded while containing unsupported rows: the curator may recall entries from parametric memory and retroactively attach page-level citations that are not the actual source. We study this hazard in Seed2Frontier discovery: the task of finding complement Wikipedia pages from a seed page to assemble a structured table. Stage-Audit addresses it with disjoint curator-auditor write rights, a row-level source-citation gate, and a 12-check audit taxonomy over keys, schema, source roles, cardinality, and scope. On a
The proliferation of LLMs creates a growing need for auditable data sourcing, especially in critical applications like structured data assembly and knowledge base construction.
This research addresses a critical vulnerability in LLM-generated information, specifically the potential for 'hallucinated' or improperly sourced data in structured tables, which can undermine trust and utility.
The proposal of 'Stage-Audit' introduces a methodological framework to improve the reliability and audibility of LLM-curated data, moving towards more trustworthy autonomous information systems.
- · AI developers focused on data integrity
- · Enterprises reliant on structured data
- · Knowledge base creators
- · Auditing and compliance sectors
- · LLM applications without robust sourcing mechanisms
- · Users relying on unverified LLM output
- · Creators of low-quality LLM-generated content
Improved reliability of LLM-generated structured data for enterprise and research applications.
Increased adoption of 'auditable AI' principles across various domains, fostering greater trust in AI systems.
Potential for new regulatory standards around AI-generated data reliability and provenance, impacting AI development cycles.
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