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About this paper
Ali Asaria, Tony Salomone, Deep Gandhi
The Transformer Lab empirically investigated `google/diffusiongemma-26B-A4B-it`, revealing its token commitment strategy is neither purely parallel nor strictly sequential but exhibits a partial, granularity-dependent left-to-right bias with sub-block disorder, while commit confidence proved predictive of correctness for math tasks but not factual recall. This work also developed a robust methodology for measuring decoding order in Diffusion Language Models (DLMs).
Key insights
- DiffusionGemma's token commitment is neither purely parallel nor strictly autoregressive; instead, it exhibits a partial, granularity-dependent left-to-right bias with genuine sub-block disorder and large, within-batch unresolved commitments.
- The model's decoding behavior is regime-dependent, showing approximately order-independent generation for structured JSON outputs, in contrast to a moderate left-to-right bias for prose and code.
- Commit confidence (negative entropy-at-commit) is a reliable predictor of correctness for certain tasks (e.g., math) but not universally across all regimes (e.g., factual recall), highlighting the task-specific nature of DLM calibration.
Summary by alphaXiv.
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