ordinal classification

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papersSEP 12 04:00 UTC

Paper proposes adaptive margin loss to curb center-class bias in ordinal classification

A new arXiv preprint introduces Adaptive Margin Ordinal Loss, a training objective aimed at a problem the authors call center-class hedging. They argue that standard cross-entropy encourages networks to favor middle categories on ordinal tasks, since that choice lowers expected error, and their method adds class-dependent margins to discourage this. The work targets research on ordinal classification, where labels have a meaningful order, such as severity ratings or age brackets.

arxiv/ordinal-classification/loss-functions/research

papersSEP 10 04:00 UTC

Study evaluates positional bias in LLMs used for ordinal classification

A systematic evaluation on arXiv examines whether large language models give consistent predictions when used as ordinal classifiers. The researchers ran controlled experiments showing that semantically equivalent changes to prompt organization, such as the ordering of labels and demonstrations, can shift model outputs. The findings highlight reliability concerns for deploying LLMs in ranking and rating tasks.

llms/evaluation/bias/classification