How LLMs Work, Explained Without Math

🌈 Abstract

The article explains how Large Language Models (LLMs) work, without using advanced mathematics. It covers the basics of how LLMs operate, including tokenization, next token prediction, and text generation. It also discusses the training process for LLMs, the limitations of simple Markov chain approaches, and the use of neural networks and the Transformer architecture. The article concludes by discussing whether LLMs exhibit true intelligence.

🙋 Q&A

[01] How LLMs Work, Explained Without Math

1. What is the basic function of an LLM?

• LLMs can only predict the next token in a sequence, not answer questions or chat directly.
• LLMs use a tokenizer to convert text into a sequence of unique token identifiers.
• LLMs make predictions about the probability of each token appearing next in the sequence.

2. How do LLMs generate longer text sequences?

• LLMs generate text by iteratively predicting the next token and adding it to the sequence.
• The selection of the next token can be controlled using hyperparameters like "temperature" to adjust the creativity of the generated text.

3. What are the limitations of a simple Markov chain approach to training LLMs?

• Markov chains have a very limited context window, only considering the last token.
• Expanding the context window using Markov chains quickly becomes computationally infeasible as the number of possible token sequences grows exponentially.

4. How do neural networks address the limitations of Markov chains for LLMs?

• Neural networks can approximate the probability distribution of the next token using a parameterized function, rather than a fixed lookup table.
• The parameters of the neural network are trained on large datasets to optimize the next token predictions.
• Neural networks with the Transformer architecture and attention mechanisms can effectively model long-range dependencies between tokens.

5. Do LLMs exhibit true intelligence?

• The author does not believe current LLMs have the ability to reason or come up with original thoughts.
• LLMs generate text by stitching together patterns from their training data, but can produce results that feel original and useful.
• The author cautions against trusting LLM outputs without human verification, due to their tendency to hallucinate.
• The author is skeptical that the current GPT architecture will achieve true intelligence, but leaves open the possibility of future innovations.