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.
Gaebler, Sharad Goel, Aziz Huq, and Prasanna Tambe
2 Pith papers cite this work. Polarity classification is still indexing.
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LLMs produce lower-fidelity summaries of identical public comments when attributed to lower-status occupations like street vendors versus financial analysts, with inconsistent race effects and no gender effects.
citing papers explorer
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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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All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs?
LLMs produce lower-fidelity summaries of identical public comments when attributed to lower-status occupations like street vendors versus financial analysts, with inconsistent race effects and no gender effects.