word-by-word generation
word-by-word generation refers to the autoregressive process where Large Language Models (LLMs) predict and output tokens sequentially, conditioning each new token on the preceding context. This mechanism underpins the perceived “thought” process of AI systems, though it is fundamentally a statistical prediction task rather than conscious reasoning.
Core Mechanisms
- Autoregressive Prediction: The model calculates the probability distribution of the next token based on the entire history of previous tokens, sampling or selecting the most likely candidate to append to the sequence AI as a Generative Prediction System: Mechanisms and Limitations Explained.
- Cross-Domain Application: The predictive power of these models extends beyond text to complex biological structures, such as predicting the impact of human genome variants DeepMind AlphaGenome Atlas: AI Predicts Human Genome Variant Impact.