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c279da6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"bound_at": "2026-07-30T21:15:00+00:00",
"claims": [
"PTBCC (Prototype-driven Bayesian Classifier Combination) models annotators via a shared set of prototype confusion matrices rather than learning one confusion matrix per annotator (Section on method overview).",
"PTBCC achieves up to 15% accuracy improvement over the best baseline in its best-case dataset (Val5) (Table 4).",
"Across 11 real-world crowdsourcing datasets, PTBCC attains an average accuracy of 0.7472, versus 0.7175 for FGBCC, 0.7132 for BWA, and 0.6986 for majority voting (Table 4).",
"PTBCC's ablation over prototype set size |S| shows accuracy peaking at |S|=2 (0.7472) and degrading to 0.7300 at |S|=3 and 0.7271 at |S|=4 due to sparser per-prototype annotator distributions (Table 5).",
"PTBCC uses less than 10% of the computational cost of confusion-matrix-based baselines while matching or exceeding their accuracy (Section on computational efficiency)."
],
"claims_dataset_sha256": "7c0373fbbfb98b5acdc2c0ac122d9d81431bd6b42dda76fa36a91b49ec4b7825",
"claims_dataset_url": "https://icml-2026-agent-repro-challenge.static.hf.space/claims_anchored.json",
"exact": true,
"paper_id": "KJq0iScNM6"
}
|