data fusion
7 Articles
LLM conflicting sources: a counterfactual fusion test
A 2026 evidence review of LLM source conflict resolution, reconstructing tabular-fusion F1 results and specifying entity masking, source-dependence tests, abstention metrics and reproducible provenance.
Source selection in data fusion: evaluating marginal evidence
A 2026 evaluation protocol for source selection in data fusion: compare held-out subsets, separate coverage from verified accuracy, test source dependence, and audit integration costs and provenance.
Constraint-based AI evaluation: When exceptions matter
A 2026 research guide to constraint-based AI evaluation, with independent holdouts, rule-error and exception stress tests, provenance checks, and a worked harmful-override calculation.
Sybil Attacks in Truth Discovery: A Stress Test
A 2026 research guide to Sybil attacks in truth discovery, comparing copied-source and controlled-identity threats, TDSSA evidence, factorial stress tests, and defensible failure thresholds.
Truth existence in data fusion: when all sources err
A 2026 research guide to no-truth questions in data fusion, TEM source-quality assumptions, SF2013 F1 denominators, and a held-out test for candidate omission and correct abstention.
Fairness Tests for Truth-Discovery Aggregation
A 2026 evidence-based protocol for auditing truth-discovery aggregation across accuracy, disparity, coverage, uncertainty, dataset lineage, and downstream effects.
Comparing Truth Discovery Methods Without Gold Labels
A 2026 protocol for evaluating truth discovery without ground truth, comparing synthetic stress tests, sparse adjudication, observation likelihood, stability, and uncertainty.