Demystifying GPT Models: How ChatGPT Transforms Conversations

Data Cleaning and Preparation:

The raw data acquired through web scraping undergoes a crucial step: cleaning and preprocessing. This is vital to ensure the quality and integrity of the training data. Several tasks are performed during data cleaning:

1. Removal of Irrelevant or Noisy Information: Filtering out non-textual elements such as advertisements or navigation menus ensures that the model focuses on meaningful textual information.
2. Elimination of Duplicate Entries: Duplicate instances can introduce biases, and removing them is essential to maintain the integrity of the training data.
3. Correction of Formatting Issues: Inconsistencies in formatting, such as HTML tags, are addressed to present clean and standardized text to the model.
4. Handling Sensitive Information: Privacy concerns are addressed by carefully stripping sensitive or personally identifiable information from the dataset.

Training Objective:

The essence of ChatGPT’s training revolves around predictive learning. The model is trained to predict the likelihood of a given word or phrase in a sequence of text, based on the context provided by surrounding words. This is achieved through presenting the model with partially masked input, where it must predict the missing word or phrase. The iterative process of predicting the next word refines the model’s understanding of language, encompassing grammar, syntax, and word associations.

Tokenization and Batching in AI:

Tokenization involves breaking down input text into smaller units or tokens, such as words or characters. This process enhances the model’s ability to process and understand individual elements effectively. Batching, the grouping of tokens into batches, facilitates parallelized computations, optimizing training efficiency. These techniques are important in handling the complexity and nuances of language during the training phase.