Papers by Kaveh Miandoab

1 papers
DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias (2023.findings-emnlp)

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Challenge: Existing methods to mitigate gender bias in pre-trained language models are often evaluated on datasets that check the extent to which the model is gender-neutral in its predictions.
Approach: They propose to use a manually curated dataset to measure gender bias and to measure useful gender knowledge.
Outcome: The proposed dataset aims to quantify gender biases and to assess their impact on useful gender knowledge.

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