@inproceedings{bharati-etal-2026-american,
    title = "The {A}merican Palimpsest: Quantifying {S}outh {A}sian {E}nglish Dialect Erasure in {LLM}s",
    author = "Bharati, Soumedhik  and
      Mandal, Shibam  and
      Ghosh, Swarup Kr  and
      Mondal, Sayani",
    editor = "Prabhakaran, Vinodkumar  and
      Dev, Sunipa  and
      Benotti, Luciana  and
      Hershcovich, Daniel  and
      Cao, Yong  and
      Zhou, Li  and
      Ma, BOlei  and
      Adebara, Ife",
    booktitle = "Proceedings of the 4th Workshop on Cross-Cultural Considerations in {NLP} ({C}3{NLP} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.c3nlp-1.8/",
    pages = "108--118",
    ISBN = "979-8-89176-420-0",
    abstract = "Large Language Models are increasingly deployed as writing assistants for usersin the Global South, yet rewriting prompts can suppress institutionalizedpostcolonial varieties. We quantify South Asian English (SAsE) dialect erasure ina state-of-the-art open-weight model using a 500-sentence diagnostic benchmark(320 lexical and 180 syntactic markers). On Llama 3.3 70B, standard grammarcorrection retains only 26.0{\%} of markers (lexical 31.2{\%}; syntactic 16.7{\%}),while formalization is more destructive (14.0{\%} overall retention). For lexicalitems, we observe Americanization in 56.2{\%} (correction) and 59.4{\%}(formalization) of cases, typically via Standard American paraphrases. A simpledialect-aware prompt raises retention to 92.0{\%} and reduces lexicalAmericanization to 6.2{\%}, although some function-word phenomena remain resistant. A stress test shows evenstronger suppression (6.7{\%} retention). We position dialect erasure withinrepresentational-harm and cultural-competence frameworks, and provide areplicable protocol for auditing writing-assistance systems."
}