ChatGPT provides personalized services based on users' interests and preferences, such as recommending movies, music, books, etc.

The training data of ChatGPT is usually a large-scale text corpus, such as Wikipedia, news reports, etc. Before training, it is necessary to preprocess these data, including word segmentation, removing stop words, and building a vocabulary.

In the pre training stage, ChatGPT uses a large-scale corpus for training, with the aim of enabling the model to learn a universal language representation. Specifically, ChatGPT uses unsupervised language modeling tasks, which predict the probability distribution of the next word given the prefix of a segment of text. This task can enable the model to learn the statistical laws and semantic information of language.

After the pre training is completed, the model needs to be fine-tuned to adapt to specific tasks. The process of fine-tuning usually includes supervised learning tasks, such as text classification, answering questions, etc. The purpose of fine-tuning is to make the model perform better on specific tasks.

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After the model training is completed, the model can be used for inference by inputting a text sequence to generate the corresponding text output. During the inference process, the model will generate the next most likely word based on the input text sequence, continuously generating the text sequence until it reaches the specified length or meets the stop condition.

ChatGPT uses the Transformer architecture, which includes multiple encoders and decoders. The encoder is used to convert input text into vector representation, while the decoder is used to convert vector representation into text output. At the same time, ChatGPT also has an autoregressive model, enabling the model to generate continuous text sequences.

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Generate highly readable original articles using ChatGPT. Normally generated AI articles by ordinary users have the characteristics of formulaic and neutral tone, which are easily recognized by algorithms. We have conducted secondary optimization to effectively avoid search engine algorithms.

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