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CAL-Log: Cost-Aware Active Learning with Logarithmic Cognitive Effort Modeling and Online Adaptation to Human Annotation Behavior
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
Active learning (AL) reduces labeled data requirements in NLP, yet most methods optimize label efficiency while ignoring annotation cost. Standard uncertainty sampling assumes uniform effort, leading to suboptimal resource allocation when documents vary in length. Supasan and Athuraliya (2026) introduced CAL-Log, a cost-aware AL variant using logarithmic cost modeling C(x)=α+β log(1+L(x)), where C(x) is the predicted annotation time for document x and L(x) is its token length, grounded in information foraging theory (Pirolli and Card, 1999) and psycholinguistic studies of human skimming (Rayner, 1998). This paper presents CAL-Log in full, extending that preliminary framework with two new contributions: temperature-scaled calibrated entropy and online per-annotator cost adaptation, which together resolve the cold-start calibration bottleneck identified in the prior work. Experiments on ten text classification benchmarks demonstrate a 3.3× speedup over BADGE (Batch Active learning by Diverse Gradient Embeddings; Ash et al., 2020) and 3.9× over Entropy sampling to reach F1=0.80, with large effect sizes (Cohen’s d>0.8). A live annotation deployment with preliminary user evaluation (N=7) confirms that the online cost model produces reading-speed classifications consistent with annotator self-reports, and that a transparency interface successfully communicates the scoring rationale to non-expert users.
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记录 #1 · 2026-07 · 最新观测版本
CAL-Log: Cost-Aware Active Learning with Logarithmic Cognitive Effort Modeling and Online Adaptation to Human Annotation Behavior
Active learning (AL) reduces labeled data requirements in NLP, yet most methods optimize label efficiency while ignoring annotation cost. Standard uncertainty sampling assumes uniform effort, leading to suboptimal resource allocation when documents vary in length. Supasan and Athuraliya (2026) introduced CAL-Log, a cost-aware AL variant using logarithmic cost modeling C(x)=α+β log(1+L(x)), where C(x) is the predicted annotation time for document x and L(x) is its token length, grounded in information foraging theory (Pirolli and Card, 1999) and psycholinguistic studies of human skimming (Rayner, 1998). This paper presents CAL-Log in full, extending that preliminary framework with two new contributions: temperature-scaled calibrated entropy and online per-annotator cost adaptation, which together resolve the cold-start calibration bottleneck identified in the prior work. Experiments on ten text classification benchmarks demonstrate a 3.3× speedup over BADGE (Batch Active learning by Diverse Gradient Embeddings; Ash et al., 2020) and 3.9× over Entropy sampling to reach F1=0.80, with large effect sizes (Cohen’s d>0.8). A live annotation deployment with preliminary user evaluation (N=7) confirms that the online cost model produces reading-speed classifications consistent with annotator self-reports, and that a transparency interface successfully communicates the scoring rationale to non-expert users.
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