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Status: Unchecked ×Keyword: prompt-based classification ×Clear all

2 claims from 1 paper

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

   Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

   Zhong, Lee, Zhang and Klein · 2021

   The authors fine-tune language models on a collection of 43 datasets with question-style label descriptions to improve zero-shot classification, and report gains on tasks the models had not seen.

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   1. UncheckedLanguage models fine-tuned on many classification datasets (meta-tuned) beat a same-sized QA model and a prior NLI-based zero-shot system on unseen tasks.“When evaluated on unseen tasks, meta-tuned models outperform a same-sized QA model and the previous SOTA zero-shot learning system based on natural language inference.”
   2. UncheckedIn this paper, meta-tuned models with 770M parameters scored 6.3% higher in AUC-ROC than 220M ones, and the authors expect larger models to do better.“Additionally, increasing parameter count from 220M to 770M improves AUC-ROC scores by 6.3%, and we forecast that even larger models would perform better.”