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This research challenges the assumption that decoupling mean predictions from concentration parameters in Dirichlet heads automatically yields meaningful uncertainty estimates in next-token prediction. By explicitly supervising lexical support on a character-level dataset, the authors demonstrate that standard implicit training fails to capture true count signals, whereas their proposed framework achieves significantly better generalization and calibration. The study concludes that while concentration parameters can be identified, they often fail to provide useful utility without rigorous protocols like confidence control and held-out label testing.
Decoupling Is Not Identification: Supervised Evidential Learning in Next-Token Prediction
arXiv:2609.26268v1 Announce Type: new Abstract: A next-token probability says what a model predicts, not how much training support lies behind it. A Dirichlet head can represent this distinction by separating mean $m$ from concentration $S$, but decoupling does not identify what $S$ means. Here we propose an Evidential Next-Token Prediction (ENTOP) framework to audit this gap on character-level Moby-Dick, using exact 8-gram count as a reproducible lexical-support label and withholding count regression from 20% of context types. Standard implicit evidential training carries essentially no count signal beyond confidence on held-out-label types (partial Spearman $\rho = 0.001 \pm 0.014$), whereas explicit supervision generalizes ($\rho = 0.201 \pm 0.010$; matched-pair win $= 0.822 \pm 0.021$). CE predictive entropy is at chance for unseen 8-grams (AUROC $= 0.490 \pm 0.004$), while supervised vacuity reaches $0.772 \pm 0.003$, comparable with an indexed CE-representation baseline ($0.769$) but below the tautological corpus oracle ($1.000$). Neither longest-suffix nor representation-distance strata explain where amortization succeeds. Increasing count weight under the digamma objective improves support fit only by sacrificing prediction. A constant predictor wins natural log-RMSE, and vacuity does not improve error deferral. These results motivate a minimum evidence protocol---confidence control, matched pairs, held-out labels, a constant baseline, and a decision test---and show that concentration can pass identification while failing calibration and utility.
Abstract:A next-token probability says what a model predicts, not how much training support lies behind it. A Dirichlet head can represent this distinction by separating mean $m$ from concentration $S$, but decoupling does not identify what $S$ means. Here we propose an Evidential Next-Token Prediction (ENTOP) framework to audit this gap on character-level Moby-Dick, using exact 8-gram count as a reproducible lexical-support label and withholding count regression from 20% of context types. Standard implicit evidential training carries essentially no count signal beyond confidence on held-out-label types (partial Spearman $\rho = 0.001 \pm 0.014$), whereas explicit supervision generalizes ($\rho = 0.201 \pm 0.010$; matched-pair win $= 0.822 \pm 0.021$). CE predictive entropy is at chance for unseen 8-grams (AUROC $= 0.490 \pm 0.004$), while supervised vacuity reaches $0.772 \pm 0.003$, comparable with an indexed CE-representation baseline ($0.769$) but below the tautological corpus oracle ($1.000$). Neither longest-suffix nor representation-distance strata explain where amortization succeeds. Increasing count weight under the digamma objective improves support fit only by sacrificing prediction. A constant predictor wins natural log-RMSE, and vacuity does not improve error deferral. These results motivate a minimum evidence protocol---confidence control, matched pairs, held-out labels, a constant baseline, and a decision test---and show that concentration can pass identification while failing calibration and utility.
Artificial Intelligence (cs.AI)
arXiv:2609.26268 [cs.AI]
(or arXiv:2609.26268v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.26268 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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