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Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology.
Language models are few-shot learners
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Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing . Association for Computational Linguistics, Online
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Are we consistently biased? multidimensional analysis of biases in distributional word vectors
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AdapterHub: A framework for adapting transformers
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Efficient transfer learning for quality estimation with bottleneck adapter layer
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Training and domain adaptation for supervised text segmentation
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