Autoregressive Generation
Autoregressive generation is a sequential prediction method where a model generates output token-by-token (or element-by-element), conditioning each new prediction on all previously generated tokens. This approach is fundamental to modern large-language-models (LLMs) and sequence-to-sequence tasks.
Core Mechanism
- Sequential Dependency: The probability of the next token is conditioned on the history : .
- Markov Assumption: Simplified models assume dependence only on a fixed window of previous tokens (e.g., N-gram Models), while transformer-based models use attention mechanisms to attend to the entire context window.
- Decoding Strategies: Common methods include Greedy Decoding, Beam Search, and Sampling (e.g., temperature, top-k, top-p).
Emerging Alternatives: Parallel Generation
While autoregressive modeling dominates current LLMs, its sequential nature imposes latency constraints. Recent research explores non-autoregressive or parallel generation methods to accelerate inference:
- Diffusion-Based Text Generation: Inspired by image synthesis, models like DiffusionGemma: Accelerating LLM Text Generation with Parallel Diffusion Architecture attempt to apply diffusion architectures to text.
- Parallel Decoding: Unlike the strict left-to-right dependency of AR models, diffusion approaches can generate multiple tokens in parallel, potentially reducing generation time significantly.
- Hybrid Architectures: Experimental models combine the coherence of autoregressive pre-training with the speed of parallel diffusion decoding steps.