Variational Neurons in Transformers for Language Modeling
Transformers for language modeling usually rely on deterministic internal computation, with uncertainty expressed mainly at the output layer. We introduce variational neurons into Transformer feed-forward computation so that uncertainty becomes part of the internal computation itself. Concretely, we replace deterministic feed-forward units with local variational units based on EVE while preserving the overall Transformer backbone. We evaluate this design in compact next-token language-modeling settings. We compare deterministic and variational variants with both predictive and probabilistic criteria. Alongside negative log-likelihood, perplexity and accuracy, we analyze calibration, condi...
| Authors | Yves Ruffenach |
|---|---|
| Primary category | cs.LG |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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