Answer: By predicting the next token based on all previous tokens using attention

## Explanation Transformer-based Large Language Models (LLMs) generate text using an **autoregressive** approach with **attention mechanisms**. Here's how it works: ### Key Mechanism: Next Token Prediction 1. **Autoregressive Generation**: LLMs generate text one token at a time, where each new token is predicted based on all previously generated tokens. 2. **Attention Mechanism**: The transformer architecture uses self-attention to weigh the importance of different tokens in the input sequence when predicting the next token. 3. **Contextual Understanding**: Unlike simple Markov models, transformers can consider long-range dependencies across the entire input sequence. ### Why Other Options Are Incorrect: - **Option A**: Classification is a different task (e.g., sentiment analysis), not text generation. - **Option C**: While LLMs can sometimes appear to copy or paraphrase, their core mechanism is prediction, not direct copying. - **Option D**: Latent vector compression is more characteristic of autoencoders or variational autoencoders, not transformer-based text generation. ### Technical Details: - The model processes input tokens through multiple transformer layers - Each layer applies self-attention to capture relationships between tokens - The final output layer produces probability distributions over the vocabulary - The model selects the next token (often using sampling techniques like temperature sampling or top-k sampling) - This process repeats until a stopping condition is met (e.g., end-of-sequence token or maximum length) This approach enables LLMs to generate coherent, contextually relevant text across various domains and tasks.

Author: Jin H