@inproceedings{giuliani-etal-2024-cava,
    title = "{CAVA}: A Tool for Cultural Alignment Visualization {\&} Analysis",
    author = "Giuliani, Nevan  and
      Ma, Cheng Charles  and
      Pradeep, Prakruthi  and
      Ippolito, Daphne",
    editor = "Hernandez Farias, Delia Irazu  and
      Hope, Tom  and
      Li, Manling",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2024.emnlp-demo.16/",
    doi = "10.18653/v1/2024.emnlp-demo.16",
    pages = "153--161",
    abstract = "It is well-known that language models are biased; they have patchy knowledge of countries and cultures that are poorly represented in their training data. We introduce CAVA, a visualization tool for identifying and analyzing country-specific biases in language models.Our tool allows users to identify whether a language model successful captures the perspectives of people of different nationalities. The tool supports analysis of both longform and multiple-choice models responses and comparisons between models.Our open-source code easily allows users to upload any country-based language model generations they wish to analyze.To showcase CAVA{'}s efficacy, we present a case study analyzing how several popular language models answer survey questions from the World Values Survey."
}