Rubric embeddings from expert criteria mitigate label bias in models trained on historical evaluations, reducing group disparities while improving cohort quality on a master's program dataset.
Interpretable-by-design text understanding with iteratively generated concept bottleneck
2 Pith papers cite this work. Polarity classification is still indexing.
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LFD discovers predictive text features via LLM contrastive proposals, cross-LLM Cohen's kappa screening, and residual held-out gain selection, matching baseline accuracy while achieving higher human agreement and lower label leakage on ten tasks.
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Mitigating Label Bias with Interpretable Rubric Embeddings
Rubric embeddings from expert criteria mitigate label bias in models trained on historical evaluations, reducing group disparities while improving cohort quality on a master's program dataset.
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Interpretable Discriminative Text Representations via Agreement and Label Disentanglement
LFD discovers predictive text features via LLM contrastive proposals, cross-LLM Cohen's kappa screening, and residual held-out gain selection, matching baseline accuracy while achieving higher human agreement and lower label leakage on ten tasks.