On three SuperGLUE binary tasks, bottleneck, Mix-and-Match, and LoRA adapters generally match or beat fine-tuning accuracy at lower training time, while prompt and prefix tuning underperform; on the news task, fine-tuning remains the most accurate.
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Comparative Analysis of Efficient Adapter-Based Fine-Tuning of State-of-the-Art Transformer Models
On three SuperGLUE binary tasks, bottleneck, Mix-and-Match, and LoRA adapters generally match or beat fine-tuning accuracy at lower training time, while prompt and prefix tuning underperform; on the news task, fine-tuning remains the most accurate.