Papers with computationally lightweight density-based
Efficient Out-of-Domain Detection for Sequence to Sequence Models (2023.findings-acl)
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Artem Vazhentsev, Akim Tsvigun, Roman Vashurin, Sergey Petrakov, Daniil Vasilev, Maxim Panov, Alexander Panchenko, Artem Shelmanov
| Challenge: | Sequence-to-sequence (seq2sequ) models are a ubiquitous tool for text generation but they are not suitable for many other tasks. |
| Approach: | They propose to use UE techniques to identify out-of-domain (OOD) inputs where the model is susceptible to errors. |
| Outcome: | The proposed methods outperform heavyweight ensembles on the task of OOD detection. |