Confidence-aware pseudo-label selection and verifier training for semi-supervised LLM reasoning with minimal labels
An adaptive threshold selection policy that chooses thresholds on validation data using pseudo-label precision and sample count is introduced and is combined with confidence-aware verifier training to support confidence-based selection of pseudo-labeled subsets.
Keizo Kato, Chenhui Chu, Yugo Murawaki et al.
· Frontiers in Artificial Inte... · 0 citations