@inproceedings{liu-etal-2025-iiet,
    title = "{IIET}: Efficient Numerical Transformer via Implicit Iterative Euler Method",
    author = "Liu, Xinyu  and
      Li, Bei  and
      Liu, Jiahao  and
      Ruan, Junhao  and
      Jiao, Kechen  and
      Tang, Hongyin  and
      Wang, Jingang  and
      Xiao, Tong  and
      Zhu, JingBo",
    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.453/",
    pages = "8955--8969",
    ISBN = "979-8-89176-332-6",
    abstract = "High-order numerical methods enhance Transformer performance in tasks like NLP and CV, but introduce a performance-efficiency trade-off due to increased computational overhead. Our analysis reveals that conventional efficiency techniques, such as distillation, can be detrimental to the performance of these models, exemplified by PCformer. To explore more optimizable ODE-based Transformer architectures, we propose the \textbf{I}terative \textbf{I}mplicit \textbf{E}uler \textbf{T}ransformer \textbf{(IIET)}, which simplifies high-order methods using an iterative implicit Euler approach. This simplification not only leads to superior performance but also facilitates model compression compared to PCformer. To enhance inference efficiency, we introduce \textbf{I}teration \textbf{I}nfluence-\textbf{A}ware \textbf{D}istillation \textbf{(IIAD)}. Through a flexible threshold, IIAD allows users to effectively balance the performance-efficiency trade-off. On lm-evaluation-harness, IIET boosts average accuracy by 2.65{\%} over vanilla Transformers and 0.8{\%} over PCformer. Its efficient variant, E-IIET, significantly cuts inference overhead by 55{\%} while retaining 99.4{\%} of the original task accuracy. Moreover, the most efficient IIET variant achieves an average performance gain exceeding 1.6{\%} over vanilla Transformer with comparable speed."
}