Large Language Models explained briefly

Key Concepts:

• Large Language Models (LLMs)
• Next-word prediction
• Probability distribution
• Parameters/Weights
• Training (Pre-training & Reinforcement Learning with Human Feedback)
• Backpropagation
• GPUs
• Transformers
• Attention mechanism
• Feed-forward neural networks
• Emergent behavior

1. How LLMs Work: Next-Word Prediction

• LLMs function by predicting the next word in a sequence of text.
• They assign a probability to each possible word, rather than predicting a single word with certainty.
• Chatbots use LLMs to generate responses by repeatedly predicting the next word based on the user's input and the model's training.
• Randomness is introduced in the selection of less likely words to make the output more natural and varied. This means the same prompt can yield different responses.

2. Training LLMs: Data and Parameter Tuning

• LLMs are trained on massive amounts of text data, typically sourced from the internet. GPT-3, for example, was trained on an amount of text that would take a human over 2600 years to read non-stop.
• Training involves tuning numerous parameters (or weights) within the model. These parameters are continuous values that determine the probabilities assigned to different words.
• Initially, parameters are set randomly, resulting in nonsensical output.
• The model is refined through backpropagation, an algorithm that adjusts parameters to increase the likelihood of predicting the correct next word based on training examples.

3. The Scale of Computation

• Training LLMs requires immense computational power.
• The video illustrates this by stating that even if one could perform one billion additions and multiplications per second, training the largest LLMs would still take well over 100 million years.
• This computation is made possible by GPUs, specialized computer chips designed for parallel processing.

4. Pre-training vs. Reinforcement Learning with Human Feedback

• The initial training phase is called "pre-training," where the model learns to autocomplete text.
• To make the model a better AI assistant, it undergoes "reinforcement learning with human feedback."
• Human workers flag problematic or unhelpful predictions, and these corrections are used to further refine the model's parameters.

5. The Transformer Architecture

• Transformers, introduced in 2017 by Google researchers, process text in parallel, unlike earlier models that processed text sequentially.
• The first step in a transformer is to associate each word with a long list of numbers (a vector). This encodes language into a numerical format suitable for the training process.
• The "attention" mechanism allows these vectors to interact and refine their meanings based on the surrounding context. For example, the vector for "bank" might be adjusted to represent "riverbank" based on the context.
• Transformers also include feed-forward neural networks, which provide additional capacity for storing patterns learned during training.
• Data flows through multiple iterations of attention and feed-forward networks, enriching the vectors with information needed to predict the next word.

6. Emergent Behavior and Challenges

• The specific behavior of an LLM is an emergent phenomenon resulting from the tuning of its parameters during training.
• This makes it difficult to understand precisely why a model makes a particular prediction.
• Despite this, LLMs can generate fluent, fascinating, and useful text.

7. Additional Resources

• The speaker references a deep learning series that explains transformers and attention in detail.
• The speaker also mentions a talk given at TNG in Munich about the same topic, available on their second channel.

8. Synthesis/Conclusion

LLMs are sophisticated systems that predict the next word in a sequence by assigning probabilities to all possible words. They are trained on vast amounts of data and refined through backpropagation and reinforcement learning with human feedback. The transformer architecture, with its attention mechanism, enables parallel processing and contextual understanding. While the emergent behavior of LLMs makes it challenging to fully understand their predictions, they are capable of generating remarkably fluent and useful text.