The article further delves into the training process of these models, explaining that they learn from large amounts of text data. The author introduces the concept of Markov chains and their limitations, particularly their inability to handle large context windows. The article then transitions to the use of neural networks, which can approximate token probabilities algorithmically, making them more scalable. The author also touches on the complexity of these models, mentioning that they consist of billions of parameters and are trained over weeks or months, making them difficult to debug or understand.
Key takeaways:
- Generative AI, specifically Large Language Models (LLMs) like GPT-2 and GPT-3, work by predicting the next word (or token) in a sequence based on the input text provided.
- These models use a basic unit of text known as a token, which can represent words, sequences of characters, or punctuation. The complete list of tokens used by an LLM forms its vocabulary.
- LLMs generate text by running in a loop, predicting the next token based on the previous ones, and adding this to the input for the next iteration. The process is controlled by hyperparameters that can influence the 'creativity' of the generated text.
- The training process for LLMs involves adjusting parameters in a neural network to improve the accuracy of token predictions. The complexity and scale of these models make them difficult to debug or understand fully.