TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
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Computer Science > Machine Learning
Title:TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
Abstract:Log-likelihood is a standard metric for evaluating generative models. Unfortunately, in contrast to autoregressive models (ARMs), discrete diffusion models generally do not admit exact computation of this quantity. Existing evaluations, therefore, rely on the evidence lower bound (ELBO), leaving unclear how much higher the true value may be. We address this by introducing the Tangent Upper Bound on Evidence (TUBE), a variational upper bound on log-likelihood that admits an unbiased Monte Carlo estimator. Our TUBE extends across latent-variable models, including masked diffusion models (MDMs), any-order ARMs (AO-ARMs), and block variants of both. Applied to block MDMs and block AO-ARMs, TUBE reveals our key empirical finding that these models lie strictly below the exact ARM baseline, showing that ARMs still dominate in likelihood.
| Comments: | Preprint. 9 pages main text, 5 figures, plus appendix |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.24292 [cs.LG] |
| (or arXiv:2605.24292v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24292
arXiv-issued DOI via DataCite (pending registration)
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