@inproceedings{pan-etal-2025-analyzing,
    title = "Analyzing Dialectical Biases in {LLM}s for Knowledge and Reasoning Benchmarks",
    author = "Pan, Eileen  and
      Choi, Anna Seo Gyeong  and
      Ter Hoeve, Maartje  and
      Seto, Skyler  and
      Koenecke, Allison",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1139/",
    doi = "10.18653/v1/2025.findings-emnlp.1139",
    pages = "20882--20893",
    ISBN = "979-8-89176-335-7",
    abstract = "Large language models (LLMs) are ubiquitous in modern day natural language processing. However, previous work has shown degraded LLM performance for under-represented English dialects. We analyze the effects of typifying ``standard'' American English language questions as non-{''}standard'' dialectal variants on multiple choice question answering tasks and find up to a 20{\%} reduction in accuracy. Additionally, we investigate the grammatical basis of under-performance in non-{''}standard'' English questions. We find that individual grammatical rules have varied effects on performance, but some are more consequential than others: three specific grammar rules (existential ``it'', zero copula, and y{'}all) can explain the majority of performance degradation observed in multiple dialects. We call for future work to investigate bias mitigation methods focused on individual, high-impact grammatical structures."
}