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Status: Unchecked ×Keyword: prefix tuning ×Clear all

2 claims from 1 paper

1. Computer Science › Domain Adaptation and Few-Shot Learning

   The Power of Scale for Parameter-Efficient Prompt Tuning

   Lester, Al‐Rfou and Constant · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 2021

   The paper introduces prompt tuning, which learns soft prompts for frozen language models, and reports it matches full model tuning at large scale and aids robustness to domain transfer.

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   1. UncheckedThe paper says its prompt tuning method, which learns soft prompts for a frozen language model, beats GPT-3's few-shot learning by a large margin.“Our end-to-end learned approach outperforms GPT-3's "few-shot" learning by a large margin.”
   2. UncheckedIn T5 experiments, prompt tuning matches full model tuning once models pass billions of parameters, though it lags behind at smaller sizes.“More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong performance of model tuning (where all model weights are tuned).”