• Corpus ID: 237142520

Mitigating harm in language models with conditional-likelihood filtration

@article{Ngo2021MitigatingHI,
  title={Mitigating harm in language models with conditional-likelihood filtration},
  author={Helen Ngo and Cooper D. Raterink and Joao M. de Ara'ujo and Ivan Zhang and Carol Chen and Adrien Morisot and Nick Frosst},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.07790}
}
Language models trained on large-scale unfiltered datasets curated from the open web acquire systemic biases, prejudices, and harmful views from their training data. We present a methodology for programmatically identifying and removing harmful text from web-scale datasets. A pretrained language model is used to assess the loglikelihood of researcher-written trigger phrases conditioned on a specific document, which is used to identify and filter documents from the dataset. We demonstrate that… 

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