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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...
Beating Full Fine-Tuning with Just 0.2% of Parameters
AdaMix is a new framework for parameter-efficient fine-tuning (PEFT) of large pretrained language models. Unlike single adaptation...
The Role of Consistency and Sharing in Efficient Fine-Tuning
This ablation study on AdaMix highlights the factors driving its efficiency in parameter-efficient fine-tuning. Results show that...
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 basel...
How Mixture-of-Adaptations Makes Language Model Fine-Tuning Cheaper and Smarter
Mixture-of-Adaptations (MoA) introduces stochastic routing, consistency regularization, and module merging to make large language...
How to Improve AI Models While Training Only 0.1% of Parameters
AdaMix is a parameter-efficient fine-tuning (PEFT) method for large language models that outperforms both full fine-tuning and exi...
