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1. 핵심 요약
- GPT-3 : 175 billions parameters
- PET (Pattern-Exploiting Training) : combines the idea of reformulating tasks as cloze questions with regular gradient-based finetuning
- In this Model : ALBERT + iPET(PET and its iterative variant)
- GPT-3보다 0.01% 더 작은 파라미터 수로 비슷한 퍼포먼스를 냄
2. 논문 링크
https://arxiv.org/abs/2009.07118
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous amounts of compute are required for training and applying such big models, resulting
arxiv.org
3. 논문 설명 링크
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
1. It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
coding-moomin.notion.site
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