TechVision

 Have you ever wondered how AI tools like ChatGPT craft sentences or translate languages? The answer lies in **autoregressive text generation**, a process powering most neural language models (LMs). Let’s explore how it works, the two flavors of text generation, and the math behind the magic.   --- ### **Two Flavors of Text Generation**   Modern LMs handle two broad tasks:   1. **Unconditional Generation** (Language Modeling):      - Goal: Generate coherent text continuations from a prefix (e.g., turning *“The cat sat on the”* into *“...mat”*).      - The model estimates probabilities over sequences: *pθ(x)*, without external guidance.   2. **Conditional Generation**:      - Goal: Generate text based on specific conditions (e.g., translating *“Hello”* to *“Hola”*).      - The model estimates *pθ(x|c)*, where *c* is a condition (like a source sentence or topic)....

**Title: How Language Models Generate Text: A Peek Under the Hood**   Have you ever wondered how AI tools like ChatGPT or Gemini craft coherent sentences, answer questions, or even write code? The secret lies in a process called **autoregressive text generation**—a method that powers most modern neural language models (LMs). Let’s break down how it works!   --- ### **Step 1: Start with a Prefix**   Imagine you type the phrase *“The cat sat on the”* into an AI chatbot. This input is called the **prefix**, and the LM’s job is to predict what comes next.   --- ### **Step 2: Predict the Next Token**   Using its neural network (often a Transformer-based architecture), the LM analyzes the prefix and generates a **probability distribution** over its fixed vocabulary. For example, it might assign:   - 60% probability to *“mat”*   - 30% to *“rug”*   - 10% to *“floor”*   This distribution reflects the model’...