@inproceedings{zhang-etal-2025-modification,
    title = "{M}o{D}ification: Mixture of Depths Made Easy",
    author = "Zhang, Chen  and
      Zhong, Meizhi  and
      Wang, Qimeng  and
      Lu, Xuantao  and
      Ye, Zheyu  and
      Lu, Chengqiang  and
      Gao, Yan  and
      Hu, Yao  and
      Chen, Kehai  and
      Zhang, Min  and
      Song, Dawei",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-long.265/",
    doi = "10.18653/v1/2025.naacl-long.265",
    pages = "5137--5149",
    ISBN = "979-8-89176-189-6",
    abstract = "Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both latency and memory. In this paper, however, we discover that MoD can barely transform existing LLMs without costly training over an extensive number of tokens. To enable the transformations from any LLMs to MoD ones, we showcase top-k operator in MoD should be promoted to threshold-p operator, and refinement to architecture and data should also be crafted along. All these designs form our method termed MoDification. Through a comprehensive set of experiments covering model scales from 3B to 70B, we exhibit MoDification strikes an excellent balance between efficiency and effectiveness. MoDification can achieve up to {\textasciitilde}1.2$\times$ speedup in latency and {\textasciitilde}1.8$\times$ reduction in memory compared to original LLMs especially in long-context applications."
}