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2 claims from 1 paper

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

   Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters

   Zhao, Tan and Mei · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 2022

   The paper proposes tiny-attention adapters, which add very small attention modules to a pretrained language model, and reports results on GLUE and FewGLUE benchmarks.

   Unchecked2 claims
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   1. Unchecked“On the GLUE benchmark, our tiny-attention adapter outperforms the other parameter-efficient transfer learning methods as well as full fine-tuning while only updating 0.05% of the parameters.”
   2. UncheckedOn the FewGLUE benchmark, the tiny-attention adapter performs comparably to GPT-3 and PET, according to the paper.“On the FewGLUE benchmark, its performance is comparable to that of GPT-3 and PET.”