Papers with BERT- multilingual
UnMASKed: Quantifying Gender Biases in Masked Language Models through Linguistically Informed Job Market Prompts (2024.eacl-srw)
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| Challenge: | Language models (LMs) often include societal biases encoded in the human-produced datasets used for their training. |
| Approach: | They evaluated six prominent language models: BERT, RoBERTa, DistilBERT, BERT- multilingual, XLM-RoBERT and DistilberT- multilinguistic. |
| Outcome: | The results show that the models generated by the models were stereotypically gendered and with a reduced bias in multilingual variants. |