@inproceedings{kim-etal-2026-query,
    title = "Query-Following vs Context-Anchoring: How {LLM}s Handle Cross-Turn Language Switching",
    author = "Kim, Kyuhee  and
      Chen, Chengheng Li  and
      Sotnikova, Anna",
    editor = "Chen, Pinzhen  and
      Zouhar, Vil{\'e}m  and
      Hu, Hanxu  and
      Khanuja, Simran  and
      Zhu, Wenhao  and
      Haddow, Barry  and
      Birch, Alexandra  and
      Aji, Alham Fikri  and
      Sennrich, Rico  and
      Hooker, Sara",
    booktitle = "Proceedings of the First Workshop on Multilingual Multicultural Evaluation",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.mme-main.13/",
    pages = "196--203",
    ISBN = "979-8-89176-368-5",
    abstract = "When multilingual users switch languages mid-conversation, how should LLMs respond? We extend MultiChallenge to evaluate cross-turn language switching, translating 182 multi-turn conversations into German, Chinese, Spanish, and Arabic. Across five frontier models, we observe asymmetric behavior: switching into a foreign language (EN{\textrightarrow}X) yields high query-language fidelity (89{--}99{\%}), but switching back to English (X{\textrightarrow}EN) reveals divergent policies. GPT-5 follows the query language ($>$95{\%}), while Claude Opus 4.5 and Command R+ maintain the established conversation language ($<$8{\%}). Task accuracy remains stable across conditions regardless of language selection differences. A simple explicit system prompt shows limited effectiveness in modifying these defaults."
}