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NOV, 26.: Data Preparation for Large Language Models and ChatGPT

Preparing data for large language models such as ChatGPT involves several important steps to ensure that the model is trained on high-quality, diverse and relevant text data. We’ll show you how.

You should go through these 7 steps very carefully one after the other:

  1. Data collection: Collect a wide range of text data from various sources such as books, articles, websites, social media platforms, forums and other publicly available text corpora. Ensure that the data covers a wide range of topics, writing styles and language patterns so that the model is robust and versatile.
  2. Data cleansing and pre-processing: Remove any distracting or irrelevant content from the text data, such as HTML tags, special characters or formatting artifacts. Standardize the text by converting it to a uniform format, including conversion to lowercase, tokenization and removal of punctuation marks. Normalizing the text by handling abbreviations, slang and other linguistic variations to ensure consistency.
  3. Data augmentation: Improve the variety and quantity of training data through data augmentation techniques such as paraphrasing, back-translation, synonym replacement and adding noise. Expanding the data helps prevent overfitting and improves the model’s ability to generalize to different language patterns and contexts.
  4. Dataset matching: Ensure that the training dataset is balanced across different topics, genres and language styles to avoid bias and ensure that the model learns to produce coherent responses across a wide range of contexts. If certain topics or areas are underrepresented in the dataset, consider expanding the data or collecting additional samples to address the imbalance.
  5. Quality assurance: Manually review and validate a subset of the training data to identify and correct errors, inconsistencies or inappropriate content. Ensure that the data meets quality standards in terms of accuracy, relevance and suitability for the intended application.
  6. Ethical rules: Consider ethical implications related to privacy, bias, fairness and security when selecting and pre-processing training data. Take measures to minimize potential risks and ensure responsible use of the language model in real-world applications.
  7. Split the dataset: Split the prepared dataset into training, validation and test datasets to evaluate the performance and generalizability of the model. Typically, the training dataset is used to train the model, the validation dataset is used to tune hyperparameters and monitor performance during training, and the test dataset is used to evaluate the performance of the model on unseen data

What can you take away from the online seminar?

We will show you how to prepare the data and use it to train the models. Become an AI expert!

Agenda Online-Seminar

  • 26/11/2024
  • 10:00 a.m. – 11:00 a.m.
  • Presented by the HICO-AI-Competence-Center Team


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