GPT-2 The Origin Story

Описание: This academic paper introduces GPT-2, a large language model designed for unsupervised multitask learning across various natural language processing tasks. The research highlights GPT-2's ability to perform diverse tasks in a "zero-shot" setting, meaning without explicit supervised training for each specific task. The model was trained on WebText, a massive and diverse dataset of human-curated webpages, which allowed it to implicitly learn tasks by predicting text. While showing state-of-the-art results on several language modeling benchmarks and competitive performance on tasks like reading comprehension, the paper also notes areas like summarization and translation where its zero-shot capabilities are still rudimentary. The authors also discuss the importance of dataset quality and the impact of data overlap in evaluating the model's generalization versus memorization.

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