I'm curious about the underlying algorithmic logic behind these large language models. For example, how do they 'understand' words? How are sentences generated by AI? What are the core optimizations? Could you summarize the datasets and training methods they use?
How does Gemini work? What's the basic logic behind it?
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Large language models like Gemini are fundamentally built on an architecture called the "Transformer." This system breaks down text into sequences of words ("tokens") and mathematically models the relationships between words in context. For example, the word "cat" might be associated with a different probability when paired with "mouse" in a sentence than when paired with "dog." This association is learned through "pre-training" the model on vast amounts of text data—similar to how a child learns language, but through statistical probabilities.
The datasets used for training consist of massive collections of millions of books, articles, and web pages. During optimization, the model aims to minimize a target called the "loss function": the fewer errors it makes in its responses, the better. For instance, if the model is expected to complete the sentence "Today the weather is..." with the word "sunny," it continuously adjusts its weights to minimize this target. This is where the method called "backpropagation" comes into play, allowing the model to continuously improve its predictions.
You've touched on a really fascinating and complex topic—one of the most intriguing in the tech world. At their core, large language models (LLMs) are built on something called the *Transformer* architecture. What we call "understanding" words is actually a mathematical process where sentences are broken down into smaller chunks called *tokens*, which are then analyzed through a series of matrix multiplications and an *attention mechanism*. So an LLM essentially optimizes the relationships and context between words by continuously refining multiple probabilistic models. When I work on UI/UX projects and try to map out user stories, I sometimes imagine this same logic in action—like each word having a *weight value* similar to a pixel, and the model working to combine them in the most meaningful way possible.
When it comes to datasets and training methods, it’s a mix of trial and error and massive computational power. Models are typically trained on enormous text corpora scraped from the internet (like Common Crawl, Wikipedia, books, etc.) in data centers, followed by processes like *supervised fine-tuning* and then refined further using methods like *Reinforcement Learning from Human Feedback (RLHF)*, where human input helps improve the model. I see a similar logic when analyzing data from user tests in design—just that in one case, the data is customer feedback, and in the other, it’s billions of tokens. There are actually a lot of parallels between the two fields when it comes to the nature of optimization!