text: 232,89,1306233895323656192,1215310334,Timo Schick,"🎉 New paper 🎉 We show that language models are few-shot learners even if they have far less than 175B parameters. Our method performs similar to @OpenAI's GPT-3 on SuperGLUE after training on 32 examples with just 0.1% of its parameter count: <LINK> #NLProc <LINK> This is achieved by combining PET (<LINK>) with pretrained ALBERT. Key factors for strong performance include concurrently using multiple ""task descriptions"" and using labeled data to perform actual parameter updates.",https://arxiv.org/abs/2009.07118,"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 in a large carbon footprint and making it difficult for researchers and practitioners to use them. We show that performance similar to GPT-3 can be obtained with language models that are much ""greener"" in that their parameter count is several orders of magnitude smaller. This is achieved by converting textual inputs into cloze questions that contain a task description, combined with gradient-based optimization; exploiting unlabeled data gives further improvements. We identify key factors required for successful natural language understanding with small language models. ","It's Not Just Size That Matters: Small Language Models Are Also Few-Shot