@inproceedings{masud-etal-2024-hate,
    title = "Hate Personified: Investigating the role of {LLM}s in content moderation",
    author = "Masud, Sarah  and
      Singh, Sahajpreet  and
      Hangya, Viktor  and
      Fraser, Alexander  and
      Chakraborty, Tanmoy",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.886/",
    doi = "10.18653/v1/2024.emnlp-main.886",
    pages = "15847--15863",
    abstract = "For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model`s (LLM) ability to represent diverse groups is unclear. By including additional context in prompts, we comprehensively analyze LLM`s sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected. Our findings on two LLMs, five languages, and six datasets reveal that mimicking persona-based attributes leads to annotation variability. Meanwhile, incorporating geographical signals leads to better regional alignment. We also find that the LLMs are sensitive to numerical anchors, indicating the ability to leverage community-based flagging efforts and exposure to adversaries. Our work provides preliminary guidelines and highlights the nuances of applying LLMs in culturally sensitive cases."
}