@inproceedings{guo-etal-2026-liveclktbench,
    title = "{L}ive{CLKTB}ench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual {LLM}s",
    author = "Guo, Pei-Fu  and
      Tsai, Yun-Da  and
      Hsu, Chun-Chia  and
      Chen, Kai-Xin  and
      Tsai, Ya An  and
      Chang, Kai-Wei  and
      Peng, Nanyun  and
      Yeh, Mi-Yen  and
      Lin, Shou-De",
    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 1: Long 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-long.694/",
    pages = "15193--15208",
    ISBN = "979-8-89176-390-6",
    abstract = "Evaluating cross-lingual knowledge transfer in large language models (LLMs) is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training. We present LiveCLKTBench, an automated generation pipeline specifically designed to isolate and measure cross-lingual knowledge transfer. Our pipeline identifies self-contained, time-sensitive knowledge entities from real-world domains, filters them based on temporal occurrence, and verifies them against the model{'}s knowledge. The documents of these valid entities are then used to generate factual questions, which are translated into multiple languages to evaluate transferability across linguistic boundaries. Using LiveCLKTBench, we evaluate several LLMs across five languages and observe that cross-lingual transfer is strongly influenced by linguistic distance and often asymmetric across language directions. While larger models improve transfer, the gains diminish with scale and vary across domains. These findings provide new insights into multilingual transfer and demonstrate the value of LiveCLKTBench as a reliable benchmark for future research."
}