Smarter Fine-Tuning for NLU and NLG Tasks

AdaMix introduces a mixture-of-adapters approach to parameter-efficient fine-tuning that consistently beats state-of-the-art baselines across major NLP benchmarks. Tested on GLUE, E2E, WebNLG, and DART, AdaMix not only matches but often outperforms full model fine-tuning with BERT, RoBERTa, and GPT-2. Its advantage extends to few-shot learning, where AdaMix narrows the performance gap with full prompt-based fine-tuning, delivering strong results with fewer labeled examples.