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  "documentTitle": "GPT-3 and the actuarial landscape",
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  "authorName": "Oliver Wyman",
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  "notes": "Explains the technical process of how GPT models convert text to tokens and predict the next token as a classification task.",
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      "text": "Fundamentally, GPT-3 and ChatGPT are neural networks that constantly give a probability to what should be the next outputted word.",
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      "text": "Example: Tokenization of an Input\nWhat do actuaries do exactly?\n[2061, 466, 43840, 3166, 466, 3446, 30]",
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      "text": "Classification Problem\nNext word prediction becomes a classification problem\nInput: series of tokens (a sentence)\nOutput: probability distribution over all tokens\nVocab size of GPT-3 = 50,257\nThe problem becomes a classification problem with 50,257 labels",
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      "text": "First Step: Tokenization\nFirst step of NLP any model is to convert text into numbers, or \"tokens\".\nGPT-3's tokenizer assign integers to chunks of characters.\nIt's a one-to-one mapping, fixed mapping.\nIn the input layer, \"exactly\" will always be mapped to the number 3446\nIn the output layer, 3446 will always be mapped to \"exactly\"",
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      "text": "Fundamentally, GPT-3 and ChatGPT are neural networks that constantly give a probability to what should be the next outputted word. That's why ChatGPT types one word at a time!",
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      "text": "Source: https://platform.openai.com/tokenizer",
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