GPT-3: Language Models are Few-Shot Learners (Paper Explained)

Yannic Kilcher explains the GPT-3 paper, detailing its architecture, dataset, and performance across various NLP tasks.

#gpt3 #openai #gpt-3 How far can you go with ONLY language modeling? Can a large enough language model perform NLP task out of the box? OpenAI take on these and other questions by training a transformer that is an order of magnitude larger than anything that has ever been built before and the results are astounding. OUTLINE: 0:00 - Intro & Overview 1:20 - Language Models 2:45 - Language Modeling Datasets 3:20 - Model Size 5:35 - Transformer Models 7:25 - Fine Tuning 10:15 - In-Context Learning 17:15 - Start of Experimental Results 19:10 - Question Answering 23:10 - What I think is happening 28:50 - Translation 31:30 - Winograd Schemes 33:00 - Commonsense Reasoning 37:00 - Reading Comprehension 37:30 - SuperGLUE 40:40 - NLI 41:40 - Arithmetic Expressions 48:30 - Word Unscrambling 50:30 - SAT Analogies 52:10 - News Article Generation 58:10 - Made-up Words 1:01:10 - Training Set Contamination 1:03:10 - Task Examples arxiv.org/abs/2005.14165 github.com/openai/gpt-3 Abstract: Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general. Authors: Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, G