Presents the first online learning-to-defer algorithm with regret bounds O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
arXiv preprint arXiv:1707.07328 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Scaling multiple-choice questions to 100 options on a Korean error detection task shows that LLM performance on conventional benchmarks overstates true competence due to shortcut strategies.
citing papers explorer
-
Online Learning-to-Defer with Varying Experts
Presents the first online learning-to-defer algorithm with regret bounds O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
-
Pushing the Boundaries of Multiple Choice Evaluation to One Hundred Options
Scaling multiple-choice questions to 100 options on a Korean error detection task shows that LLM performance on conventional benchmarks overstates true competence due to shortcut strategies.