I tiakina i:
| Kaituhi matua: | |
|---|---|
| Hōputu: | Recurso digital |
| Reo: | Ingarihi |
| I whakaputaina: |
Zenodo
2026
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| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.5281/zenodo.19433529 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
Rārangi ihirangi:
- <p>We extend the Livnium attractor-based NLI system with three contributions: (1) a cross-encoder upgrade improving SNLI dev accuracy from 82.2% to 84.5% via joint [CLS] premise [SEP] hypothesis [SEP] encoding; (2) token-level alignment extraction from the last-layer BERT cross-attention block, rendering the model's internal structural comparison visible as a force map between premise and hypothesis tokens; and (3) an alignment divergence metric that serves as a zero-cost intrinsic reliability signal, with empirically validated thresholds distinguishing stable from unreliable predictions. We further demonstrate that the same constraint-injection mechanism reproduces the Bayesian belief update in the Monty Hall problem, connecting NLI inference to classical decision theory through a unified energy-reshaping framework.</p>