@inproceedings{ukarapol-etal-2026-language,
    title = "Language-Aware Token Boosting: {LLM} Language Confusion Reduction Without Tuning",
    author = "Ukarapol, Trapoom  and
      Sarapat, Pakhapoom  and
      Chukamphaeng, Nut",
    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 2: Short 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-short.40/",
    pages = "481--489",
    ISBN = "979-8-89176-391-3",
    abstract = "Large language models (LLMs) sometimes exhibit language confusion when generating non-English text. Existing approaches typically rely on fine-tuning to mitigate this issue. In contrast, we propose a tuning-free paradigm for reducing language confusion. Within this paradigm, we introduce two methods: Language-Aware Token Boosting (LATB), which applies targeted perturbations to tokens associated with the desired language, and Adaptive Language-Aware Token Boosting (Adaptive-LATB), which dynamically adjusts these perturbations based on the model{'}s confidence in the intended language. Experiments demonstrate that our methods effectively improve multilingual alignment by reducing language confusion, while maintain the summarization quality without requiring any additional fine-tuning. Our code is publicly available.[{\ensuremath{<}}https://github.com/scbdatax/genai-datax-language-aware-token-boosting{\ensuremath{>}}]."
}