The author also explains the concept of autoregressive LLMs, which involves continuously predicting the next token and appending it to the current prompt until the end of sentence token is predicted. The author concludes by suggesting that readers can further explore how LLMs are trained and dive into Transformers, the main building block for modern LLMs. The author also offers consultancy services and runs a development agency, providing software development services and consultation for projects.
Key takeaways:
- Large Language Models (LLMs) work by converting text into embeddings, forwarding these embeddings through the hidden layers of a neural network, and using the logits of the last layer to predict the next token in the sequence.
- Embeddings are a fundamental concept in the field of natural language processing (NLP), LLMs, and AI broadly. They capture the semantic and contextual meanings of words and fragments (referred to as tokens) such that the relationships between these tokens are captured.
- One-hot encoding is a technique used in old-school natural language processing systems to represent words as sparse, high-dimensional vectors, where each word is represented as its unique index in the large vocabulary space.
- Autoregressive behavior in LLMs involves predicting the next token, appending this predicted next token to the current prompt, and repeating this process until the end of sentence token is predicted.