Clinical de-identification relies on accurately identifying personally identifiable information (PII). However, manually annotated datasets are costly to construct, while existing synthetic alternatives often provide limited details about their generation process or rely on relatively simple synthesis strategies. We in...
Linh Le, Christian Hoang, Huy Hoang Ha· 0 citations
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, standard benchmarks for machine translation are approaching saturation. Further, automatic translation metrics are unreliable, opaque, and vulne...
Vilém Zouhar, Niyati Bafna, Mukund Choudhary et al.· 0 citations
The results show that robustness claims based on a single behavioral or representational metric can be misleading, and motivate multi-level evaluation of how perturbations alter language-model computation.
Dun-Li Chan, Emily Liu, Niyathi Allu et al.· 0 citations
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