If the first episode defined what an LLM is, this episode explains how it actually processes information. We dive into the mathematical framework that transforms human language into structured data, reframing creativity as a probabilistic prediction task.
Join us as we:
• Decode the Input: We explore how raw text is converted into numerical sequences called "tokens" using subword algorithms like Byte Pair Encoding, balancing efficiency with expressiveness.
• Formalize the Objective: We examine the core mechanism of "next-token prediction," revealing how models treat language not as ideas, but as a chain of conditional probabilities.
• Bridge the Gap: We contrast early N-gram models, which relied on counting, with modern neural approaches that use vector embeddings to generalize and "understand" context.
• Measure the Surprise: We unpack the metrics of Entropy and Perplexity, explaining how engineers mathematically quantify a model's uncertainty and fluency.
This episode provides the essential statistical vocabulary needed to understand how a machine "learns" to write.
