semi-autoregressive models

**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.