@inproceedings{park-etal-2025-unifying,
    title = "Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models",
    author = "Park, Seungcheol  and
      Bae, Jeongin  and
      Kwon, Beomseok  and
      Kim, Minjun  and
      Kim, Byeongwook  and
      Kwon, Se Jung  and
      Kang, U  and
      Lee, Dongsoo",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1382/",
    pages = "28468--28488",
    ISBN = "979-8-89176-251-0",
    abstract = "How can we quantize large language models while preserving accuracy? Quantization is essential for deploying large language models (LLMs) efficiently. Binary-coding quantization (BCQ) and uniform quantization (UQ) are promising quantization schemes that have strong expressiveness and optimizability, respectively. However, neither scheme leverages both advantages. In this paper, we propose UniQuan$_F$ (Unified Quantization with Flexible Mapping), an accurate quantization method for LLMs. UniQuan$_F$ harnesses both strong expressiveness and optimizability by unifying the flexible mapping technique in UQ and BCQ{'}s non-uniform quantization levels. We propose unified initialization, and local and periodic mapping techniques to optimize the parameters in UniQuan$_F$ precisely. After optimization, our unification theorem removes computational and memory overhead, allowing us to utilize the superior accuracy of UniQuan$_F$ without extra deployment costs induced by the unification. Experimental results demonstrate that UniQuan$_F$ outperforms existing UQ and BCQ methods, achieving up to 4.60{\%} higher accuracy on GSM8K benchmark."
}