@inproceedings{naseem-etal-2026-large,
    title = "Do Large Language Models Reflect Demographic Pluralism in Safety?",
    author = "Naseem, Usman  and
      Kashyap, Gautam Siddharth  and
      Ray, Sushant Kumar  and
      Ali, Rafiq  and
      Shabbir, Ebad  and
      Mohammad, Abdullah",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.107/",
    pages = "2042--2052",
    ISBN = "979-8-89176-386-9",
    abstract = "Large Language Model (LLM) safety is inherently pluralistic, reflecting variations in moral norms, cultural expectations, and demographic contexts. Yet, existing alignment datasets such as Anthropic-HH and DICES rely on demographically narrow annotator pools, overlooking variation in safety perception across communities. Demo-SafetyBench addresses this gap by modeling demographic pluralism directly at the prompt level, decoupling value framing from responses. In Stage I, prompts from DICES are reclassified into 14 safety domains (adapted from BeaverTails) using Mistral-7B-Instruct-v0.3, retaining demographic metadata and expanding low-resource domains via Llama-3.1-8B-Instruct with SimHash-based deduplication, yielding 43,050 samples. In Stage{~}II, pluralistic sensitivity is evaluated using LLMs-as-Raters{---}Gemma-7B, GPT-4o, and LLaMA-2-7B{---}under zero-shot inference. Balanced thresholds (delta = 0.5, tau = 10) achieve high reliability (ICC = 0.87) and low demographic sensitivity (DS = 0.12), confirming that pluralistic safety evaluation can be both scalable and demographically robust. Code and data available at: https://github.com/usmaann/Demo-SafetyBench"
}