Overview
This lecture explains the basics of large language models (LLMs), how they predict and generate text, the transformative role of the Transformer architecture, and the distinction between web-based and code-based LLMs.
Intuition Behind Large Language Models
- LLMs work because language is predictable; words often appear together in meaningful, patterned ways.
- Predictability in language is quantified using probabilities assigned to words or sequences of words.
Types of Language Models
- The simplest model, the unigram language model, assigns probabilities to individual words based on frequency in large text collections (corpora).
- Bigram and trigram models extend this by considering the probability of a word given one or two preceding words, improving coherent text generation.
- Extending to higher-order n-gram models (more previous words) quickly becomes computationally impractical due to the vast number of combinations.
Large Language Models and the Internet
- Modern LLMs are trained on massive text datasets from the internet, learning billions of probabilities between word sequences.
- This training enables them to generate human-like text responses to prompts.
Model Architectures and the Transformer
- Early LLMs used recurrent neural networks (RNNs), but these struggled with efficiency and vanishing probability issues.
- Transformers, introduced by Google, use an "attention" mechanism to relate each word to every other word in the input, greatly improving model performance.
- Attention allows the model to focus on relevant words and relationships without tracking the entire history, making computations more efficient.
Categories and Examples of LLMs
- Popular web interfaces like ChatGPT, Google Bard (now Gemini) make LLMs accessible without coding.
- Many LLMs (GPT, Elmo, Bert, Claude, Llama) require coding to use; these can be integrated into business systems for custom tasks.
- Platforms like Hugging Face provide access to hundreds of thousands of pre-trained models for tasks like text generation, translation, and classification.
Key Terms & Definitions
- Large Language Model (LLM) — A machine learning system trained to predict the next word(s) in text, based on massive datasets.
- Unigram Model — A model assigning probabilities to single words regardless of context.
- Bigram/Trigram/N-gram Model — Models that assign probabilities to word sequences of length two, three, or n, based on previous words.
- Transformer — A neural network architecture that uses attention mechanisms to efficiently model relationships between all words in a sequence.
- Attention — A technique allowing models to weigh the importance of each word relative to every other word in the input.
Action Items / Next Steps
- Review Transformer and attention mechanisms in more detail in the next lecture.
- Explore platforms like Hugging Face to see practical applications and available models.