@inproceedings{xu-etal-2025-linguistic,
    title = "Linguistic Neuron Overlap Patterns to Facilitate Cross-lingual Transfer on Low-resource Languages",
    author = "Xu, Yuemei  and
      Xu, Kexin  and
      Zhou, Jian  and
      Hu, Ling  and
      Gui, Lin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1407/",
    pages = "27646--27661",
    ISBN = "979-8-89176-332-6",
    abstract = "The current Large Language Models (LLMs) face significant challenges in improving their performance on low-resource languagesand urgently need data-efficient methods without costly fine-tuning.From the perspective of language-bridge,we propose a simple yet effective method, namely BridgeX-ICL, to improve the zero-shot Cross-lingual In-Context Learning (X-ICL) for low-resource languages. Unlike existing works focusing on language-specific neurons,BridgeX-ICL explores whether sharingneurons can improve cross-lingual performance in LLMs.We construct neuron probe data from the ground-truth MUSE bilingual dictionaries, and define a subset of language overlap neurons accordingly to ensure full activation of these anchored neurons.Subsequently, we propose an HSIC-based metric to quantify LLMs' internal linguistic spectrumbased on overlapping neurons, guiding optimal bridge selection.The experiments conducted on 4 cross-lingual tasks and 15 language pairs from 7diverse families, covering both high-low and moderate-low pairs, validate the effectiveness of BridgeX-ICL and offer empirical insights into the underlying multilingual mechanisms of LLMs. The code is publicly available at https://github.com/xuyuemei/BridgeX-ICL."
}