Semi-Autoregressive Models are text generation models that generate multiple tokens per decoding step — instead of producing one token at a time (fully autoregressive) or all tokens at once (fully non-autoregressive), semi-AR models generate blocks or groups of tokens at each step, balancing speed and quality.

Semi-AR Approaches

• Block-wise Generation: Generate $k$ tokens per step — reduces decoding from $N$ steps to $N/k$ steps.
• Chunk-wise: Divide the output into chunks — generate each chunk autoregressively, chunks in parallel.
• Adaptive: Dynamically determine how many tokens to generate per step — more tokens when confident, fewer when uncertain.
• Insertion-Based: Generate by inserting tokens into a growing sequence — multiple insertions per step.

Why It Matters

• Speed-Quality Trade-off: Semi-AR achieves near-AR quality with significantly faster decoding — practical for real-time applications.
• Controllable: The block size $k$ controls the speed-quality trade-off — larger $k$ = faster but potentially lower quality.
• Practical: Many deployed NLP systems use semi-AR methods — balancing latency requirements with output quality.

Semi-Autoregressive Models are the middle ground — generating multiple tokens per step to achieve faster decoding than autoregressive models without sacrificing too much quality.