This repository hosts code and datasets relating to Responsible NLP projects from Meta AI.

Projects

• AdvPromptSet
  • AdvPromptSet: a comprehensive and challenging adversarial text prompt set with 197,628 prompts of varying toxicity levels and more than 24 sensitive demographic identity groups and combinations.
• fairscore:
  • From Rebecca Qian, Candace Ross, Jude Fernandes, Eric Smith, Douwe Kiela, Adina Williams. Perturbation Augmentation for Fairer NLP. 2022.
  • PANDA, an annotated dataset of 100K demographic perturbations of diverse text, rewritten to change gender, race/ethnicity and age references.
  • The perturber, pretrained models, code and other artifacts related to the Perturbation Augmentation for Fairer NLP project will be released shortly.
• gender_gap_pipeline:
  • The Gender-GAP Pipeline, from Benjamin Muller, Belen Alastruey, Prangthip Hansanti, Elahe Kalbassi, Christophe Ropers, Eric Michael Smith, Adina Williams, Luke Zettlemoyer, Pierre Andrews, Marta R Costa-jussà
• holistic_bias:
  • From Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, Adina Williams. "I'm sorry to hear that": finding bias in language models with a holistic descriptor dataset. 2022.
  • Code to generate a dataset, HolisticBias, consisting of nearly 600 demographic terms in over 450k sentence prompts
  • Code to calculate Likelihood Bias, a metric of the amount of bias in a language model, defined on HolisticBias demographic terms
• robbie:
  • ROBBIE: we test 6 bias/toxicity metrics (including 2 novel ones) across 5 model families and 3 bias/toxicity mitigation techniques, and show that using a broad array of metrics enables much better assessment of safety issues in these models and mitigations.
• SMART-Filtering
  • from Vipul Gupta, Candace Ross, David Pantoja, Rebecca J. Passonneau, Megan Ung, Adina Williams. Improving Model Evaluation using SMART Filtering of Benchmark Datasets. 2024.
  • SMART Filtering: a new approach to select high quality subset of examples from existing benchmark datasets. The methodology applies three filtering steps: 1) removing easy examples, 2) removing data-contaminated examples that are highly likely to have been leaked into the training datasets, and 3) removing similar examples.

---

See CONTRIBUTING.md for how to help out, and see LICENSE for license information.