A positive-unlabeled learning approach using partial optimal transport is introduced to audit and correct biases in LLM-as-a-judge systems by aligning limited human positives with unlabeled outputs in embedding space.
Impact of positional encoding: Clean and adversarial rademacher complexity for transformers under in-context regression.arXiv preprint arXiv:2512.09275,
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Quantifying and Auditing LLM Evaluation via Positive--Unlabeled Learning
A positive-unlabeled learning approach using partial optimal transport is introduced to audit and correct biases in LLM-as-a-judge systems by aligning limited human positives with unlabeled outputs in embedding space.