How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking

arXiv:2312.01619v3 Announce Type: replace
Abstract: This paper presents LEMR (Label-Efficient Model Ranking) and introduces the MoraBench Benchmark. LEMR is a novel framework that minimizes the need for costly annotations in model selection by strategically annotating instances from an unlabeled validation set. To evaluate LEMR, we leverage the MoraBench Benchmark, a comprehensive collection of model outputs across diverse scenarios. Our extensive evaluation across 23 different NLP tasks in semi-supervised learning, weak supervision, and prompt selection tasks demonstrates LEMR's effectiveness in significantly …

abstract annotations arxiv benchmark cs.lg design framework instances labels model selection novel paper ranking set space type validation