Papers with fully unsupervised
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Neural machine translation models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. |
| Approach: | They propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model. |
| Outcome: | The proposed detector outperforms existing models and is competitive with detectors that employ external models trained on millions of samples. |