This 2019 paper, "Language Models are Unsupervised Multitask Learners," introduces GPT-2, a large language model designed for zero-shot learning, meaning it can perform tasks without explicit, task-specific training. The research highlights the model's ability to learn various natural language processing (NLP) tasks, such as question answering, summarization, and translation, by being trained on a diverse and extensive dataset called WebText, composed of millions of high-quality webpages. The paper demonstrates that increasing the model's capacity significantly improves performance across these tasks, often achieving state-of-the-art results in a zero-shot setting. While showing promising results, the authors acknowledge that GPT-2's practical applications are still developing, particularly in areas like summarization and translation where performance remains rudimentary compared to human benchmarks

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