Text Generation Before Transformers: Building a Markov Chain in 200 Lines of Python
This article explores building a simple Markov chain model for text generation using Python, without relying on complex deep learning models like transformers.
Why it matters
Understanding Markov chains provides valuable insight into the fundamentals of text generation before the rise of deep learning models.
Key Points
- 1Markov chains are a simple and comprehensible approach to text generation
- 2The model is a dictionary that stores the frequency of tokens following n-grams
- 3Generation is done by sampling from the frequency distributions of next tokens
- 4Markov chains have limitations compared to modern language models
Details
The article introduces a Python CLI tool called 'markov-gen' that trains a Markov chain model on any text file, saves it as JSON, and generates new text from it. Markov chains are a classic approach to text generation that predate modern deep learning models. They work by looking at the frequencies of short phrases (n-grams) and their subsequent tokens in the training corpus, and then generating new text by sampling from those frequency distributions. This simple algorithm allows you to 'see every step' of the text generation process, unlike more complex models like transformers. The article discusses the tradeoffs between the simplicity of Markov chains and their limitations compared to modern language models that can learn more sophisticated representations and patterns.
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