Autoregressive Model
An autoregressive model generates sequences token by token, where each new token depends on all previous ones – the architecture behind GPT, LLaMA, and all modern LLMs.
Autoregressive models generate token by token sequentially – the paradigm behind GPT, LLaMA, and all LLMs, making "next token prediction" the most powerful technique in AI.
Explanation
The model learns P(x_t | x_1...x_{t-1}) – the conditional probability of the next token. At inference, tokens are sampled one by one. Strengths: Natural sequence generation. Weaknesses: Slow (serial), no backward editing possible.
Marketing Relevance
Fundamental to everything LLM-based: text generation, code, chat – understanding AI marketing requires knowing the autoregressive paradigm.
Example
ChatGPT generates responses word by word – each new word is based on the entire preceding context (prompt + response so far).
Common Pitfalls
Cannot "go back" and correct earlier tokens. Errors propagate. Latency grows linearly with output length.
Origin & History
Autoregressive models have roots in statistics (AR processes, 1927). RNNs and LSTMs were early neural AR models. GPT-1 (2018) combined autoregression with transformer architecture. GPT-3 (2020) scaled to 175B parameters. GPT-4 (2023) proved that the autoregressive paradigm leads to emergent capabilities.
Comparisons & Differences
Autoregressive Model vs. Diffusion Model
AR models generate sequentially (token by token); diffusion models generate all pixels in parallel through iterative denoising.
Autoregressive Model vs. Masked Language Model (BERT)
AR models see only previous tokens (unidirectional); masked LMs see full context (bidirectional) but generate worse.