New Filipino benchmark pairs show multilingual models choose sexist and anti-queer sentences measurably more often than neutral alternatives, with models trained on more Filipino text showing the strongest bias.
In Find- ings of the Association for Computational Linguistics: NAACL 2022, pages 921–932, Seattle, United States
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Filipino Benchmarks for Measuring Sexist and Homophobic Bias in Multilingual Language Models from Southeast Asia
New Filipino benchmark pairs show multilingual models choose sexist and anti-queer sentences measurably more often than neutral alternatives, with models trained on more Filipino text showing the strongest bias.