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Topic: Domain Adaptation and Few-Shot Learning ×Clear all

4 claims from 2 papers

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

   Discernment and Social Learning as a Companion Training Layer

   Ouyang, Wu, Jiang et al. · arXiv (Cornell University) · 2022

   The authors fine-tuned GPT-3 with human demonstrations and feedback to make InstructGPT models, which they report follow user intent better, with gains in truthfulness and less toxic output.

   Unchecked2 claims
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   1. UncheckedIn human evaluations on the authors' prompts, a 1.3B-parameter InstructGPT model's outputs were preferred to those of the 175B-parameter GPT-3.“In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters.”
   2. UncheckedInstructGPT models were more truthful and less toxic than GPT-3, with only minimal performance losses on public NLP datasets.“Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.”

2. 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

   Unchecked2 claims
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   1. Unchecked“Our end-to-end learned approach outperforms GPT-3's "few-shot" learning by a large margin.”
   2. Unchecked“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 mode…