@inproceedings{das-2026-quantifying,
    title = "Quantifying Cross-Lingual Interference: Algorithmic Standardization of Kamtapuri in Large Language Models",
    author = "Das, Roumak",
    editor = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Plum, Alistair  and
      Rayson, Paul  and
      Mitkov, Ruslan  and
      Gaber, Mohamed  and
      Premasiri, Damith  and
      Tan, Fiona Anting  and
      Uyangodage, Lasitha",
    booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.loreslm-1.10/",
    pages = "110--113",
    ISBN = "979-8-89176-377-7",
    abstract = "Multilingual Large Language Models (LLMs) often demonstrate impressive zero-shot capabilities on low-resource languages. However, for languages that share a script and significant lexical overlap with a high-resource language (HRL), models may exhibit negative transfer. Focusing on Kamtapuri (Rajbanshi), a distinct low-resource language of North Bengal, we investigate the extent to which SOTA models (e.g., GPT-5.1, Gemini 2.5) preserve distinct dialectal features versus reverting to the dominant language{'}s norms. We introduce the Kamta-Shibboleth-100 (Benchmark available at: https://github.com/kamtapuri-research/Kamta-Shibboleth-100-BENCHMARK), a diagnostic benchmark derived from a curated 400k-token corpus. Our evaluation reveals a significant discrepancy: while models show high receptive understanding (up to 88{\%} translation accuracy), they exhibit a 0{\%} Syntactic Competence Rate in zero-shot generation of distinct Kamtapuri morphology, compared to 96{\%}+ accuracy on a Standard Bengali control set. Even with 5-shot prompting, syntactic accuracy improves only to 10{\%}, while the Substitution Erasure Rate (SER) reaches 71{\%}, systematically replacing Kamtapuri vocabulary with Bengali cognates. We characterize this behavior not as a lack of knowledge, but as a strong alignment bias toward high-resource standards."
}