How ChatGPT and other LLMs work - A 1000 ft view - DEV Community
Artificial Intelligence (AI) has been a part of computer science for decades, working alongside Machine Learning and Computer Vision in applications such as recommendations, search engines, classifications, and more.
The recent focus in AI is on Generative AI, which involves generating new content rather than analyzing existing content. This includes text generation, image generation, audio generation, video generation, and more. ChatGPT falls under the category of Large Language Models (LLMs) within Generative AI.
LLMs are comprised of three main components: data, architecture, and training. For example, when creating a ChatGPT-grade LLM like "Chiti," the first step involves gathering a massive amount of text data for pre-training. This pre-training phase helps the model learn language patterns, grammar, facts, and some level of reasoning.
Once the dataset is ready, the next crucial step is designing the architecture, which involves Neural Networks (NN) to understand, learn, and respond to data. Modern LLMs use Transformers, a core concept that plays a significant role in model development.
Training the LLM is a critical and resource-intensive process that influences the quality of the model's responses. The dataset is split into training and testing sets, where the training data includes input-output pairs like "Tajmahal is in" - "Agra" to help the Transformer NN learn and understand patterns.
After training, the LLM is tested with the test data to evaluate its performance. While this overview provides a high-level understanding of how LLMs work, it's important to note that there are many underlying concepts like tensors, backpropagation, position encodings, layers, and more, which will be covered in future articles.
Developing a good understanding of mathematics, particularly matrices, is essential for working with Neural Networks. Learning about these concepts is crucial for anyone interested in AI and ML.
If you have any questions or feedback, feel free to reach out. Stay tuned for more in-depth articles on AI and ML concepts in the future.
Note: Neural Networks require a good understanding of mathematics, especially matrices. If you are a student, attending math lectures is crucial for grasping these concepts effectively.
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