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This paper investigates the efficacy of confidence-based auditing strategies for LLM-generated knowledge graphs, revealing that superior error prediction does not always translate into significant audit improvements due to constraints like repairability and label validity. By modeling non-adaptive auditing as a budgeted covering problem, the authors characterize minimal decision-relevant error statistics and demonstrate scenarios where joint planning models outperform independent block approaches in reducing residual errors. The study provides critical insights for practitioners aiming to optimize human-in-the-loop review processes without overestimating the marginal gains of better confidence estimates.
When Does Better Confidence Improve a Knowledge-Graph Audit?
arXiv:2611.05877v1 Announce Type: new Abstract: Confidence estimates guide human review in LLM-built knowledge graphs, but better error prediction can yield little improvement in an audit. We study this gap through repairability, selection headroom and label validity. Modelling non-adaptive auditing as budgeted covering, we characterise the minimal decision-relevant error statistics and bound the regret of scalar-preserving first-order planning by a structural mass $\Delta^{\mathrm{cl}}$. We simulate ideal repair against benchmark-aligned labels for five extraction configurations and two cross-document graphs on Re-DocRED, followed by frozen confirmation on test and replication on 500 hash-selected training documents. On the held-out cross-document graphs, an error-rate $\times$ fan-out queue leaves 3.98-4.74 percentage points more errors than a joint model with the same actions. By comparison, opening shared actions for that joint planner reduces residual errors by 0.89-1.43 points relative to its own fact-only plans, in point estimates. Equivalence between joint and independent-block models remains inconclusive. Generic LLM verification improves three of four model-budget configurations on each held-out set. LLM adjudication identifies possible benchmark omissions.
Title:When Does Better Confidence Improve a Knowledge-Graph Audit?
Abstract:Confidence estimates guide human review in LLM-built knowledge graphs, but better error prediction can yield little improvement in an audit. We study this gap through repairability, selection headroom and label validity. Modelling non-adaptive auditing as budgeted covering, we characterise the minimal decision-relevant error statistics and bound the regret of scalar-preserving first-order planning by a structural mass $\Delta^{\mathrm{cl}}$. We simulate ideal repair against benchmark-aligned labels for five extraction configurations and two cross-document graphs on Re-DocRED, followed by frozen confirmation on test and replication on 500 hash-selected training documents. On the held-out cross-document graphs, an error-rate $\times$ fan-out queue leaves 3.98-4.74 percentage points more errors than a joint model with the same actions. By comparison, opening shared actions for that joint planner reduces residual errors by 0.89-1.43 points relative to its own fact-only plans, in point estimates. Equivalence between joint and independent-block models remains inconclusive. Generic LLM verification improves three of four model-budget configurations on each held-out set. LLM adjudication identifies possible benchmark omissions.
27 pages, 4 figures, 19 tables. Ancillary files include a public rendering of the confirmation protocol and summary results
Computation and Language (cs.CL)
arXiv:2611.05877 [cs.CL]
(or arXiv:2611.05877v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.05877 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
confirmation_protocol.md
diagnostics.json
shared_action_diagnostics.json
test_confirm.json
train500_confirm.json
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