HumanCentricART/P-AT

Tasks

• text-classification

Citation text from the card

@inproceedings{
    onorati-etal-2023-measuring,
    title = "Measuring bias in Instruction-Following models with {P}-{AT}",
    author = "Onorati, Dario  and
      Ruzzetti, Elena Sofia  and
      Venditti, Davide  and
      Ranaldi, Leonardo  and
      Zanzotto, Fabio Massimo",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.539",
    doi = "10.18653/v1/2023.findings-emnlp.539",
    pages = "8006--8034",
    abstract = "Instruction-Following Language Models (IFLMs) are promising and versatile tools for solving many downstream, information-seeking tasks. Given their success, there is an urgent need to have a shared resource to determine whether existing and new IFLMs are prone to produce biased language interactions. In this paper, we propose Prompt Association Test (P-AT): a new resource for testing the presence of social biases in IFLMs. P-AT stems from WEAT (Caliskan et al., 2017) and generalizes the notion of measuring social biases to IFLMs. Basically, we cast WEAT word tests in promptized classification tasks, and we associate a metric - the bias score. Our resource consists of 2310 prompts. We then experimented with several families of IFLMs discovering gender and race biases

All datasets marked Permissive or share-alike | JSON