Novel fine tuning techniques
Adapters and prompt tuning
re-scoring approachesÂ
data augmentation
zero-shot/few-shot transfer
test-time adaptation
pseudo-label training
ensembling
Detailed analysis of performance with pretrained models with under-represented datasets
Open sourcing new low-resource datasets and studying the performance of large pre-trained models