@inproceedings{do-etal-2026-unispec,
    title = "{U}ni{S}pec: Training-Free Speculative Decoding for Robust {LLM} Acceleration Across Languages and Hardware",
    author = "Do, Truong Dinh  and
      Le, Nguyen-Khang  and
      Nguyen, Le-Minh",
    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.285/",
    pages = "6288--6310",
    ISBN = "979-8-89176-390-6",
    abstract = "Speculative decoding accelerates large language model (LLM) inference through a draft-and-verify paradigm, yet existing methods face three key limitations: reliance on fixed draft templates that ignore device-specific verification costs, lack of mechanisms to assess draft token quality, and suboptimal tree expansion strategies. We introduce UniSpec, a training-free, lossless speculative decoding framework that enables robust, plug-and-play LLM acceleration across diverse hardware configurations and languages. UniSpec incorporates three novel components: (1) a device-aware calibration mechanism that determines the optimal draft size by measuring the acceptance-time trade-off on each target device; (2) a confidence score estimation module that assigns quality scores to n-grams based on the verifier{'}s token probabilities, enabling selective retention of high-quality draft candidates; and (3) an improved tree expansion strategy that broadens first-level exploration and applies threshold-based filtering to prune low-confidence nodes. To comprehensively evaluate multilingual performance, we create a comprehensive benchmark, covering seven languages across seven generation tasks. Experiments with various LLM architectures, hardware environments, and languages demonstrate that UniSpec consistently outperforms existing training-free methods, achieving speedups of up to 2.6x while maintaining output quality identical to standard autoregressive decoding. Our code and benchmark are publicly available."
}