Smarter AI Training with Few-Shot Natural Language Tasks

AdaMix, a parameter-efficient fine-tuning method, outperforms full model fine-tuning in few-shot NLU tasks across benchmarks like GLUE. Using prompt-based strategies without extra validation or unlabeled data, AdaMix consistently boosts performance with both BERT and RoBERTa encoders, demonstrating stability and efficiency in few-shot scenarios.