@inproceedings{kambhatla-etal-2026-improving,
    title = "Improving the Distributional Alignment of {LLM}s using Supervision",
    author = "Kambhatla, Gauri  and
      Gautam, Sanjana  and
      Zhang, Angela  and
      Liu, Alexander  and
      Srinivasan, Ravi  and
      Li, Junyi Jessy  and
      Lease, Matthew",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1583/",
    pages = "34277--34305",
    ISBN = "979-8-89176-390-6",
    abstract = "The ability to accurately align LLMs with diverse population groups on subjective questions would have great value. In this work, we show that adding simple supervision can more consistently improve the alignment of LLM-generated distributions with diverse population groups, as measured across three datasets spanning public health, public opinion, and values and beliefs. Beyond evaluating average alignment, we also report how alignment varies across specific groups. Our broad findings provide insights into the distributional alignment of LLM generations with diverse populations. By conducting evaluation over many LLMs and prompting strategies, we provide a benchmark to stimulate future research."
}