@inproceedings{wen-etal-2025-knowledge,
    title = "Knowledge Distillation for Language Models",
    author = "Wen, Yuqiao  and
      Shi, Freda  and
      Mou, Lili",
    editor = "Lomeli, Maria  and
      Swayamdipta, Swabha  and
      Zhang, Rui",
    booktitle = "Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-tutorial.4/",
    doi = "10.18653/v1/2025.naacl-tutorial.4",
    pages = "25--29",
    ISBN = "979-8-89176-193-3",
    abstract = "Knowledge distillation (KD) aims to transfer the knowledge of a teacher (usually a large model) to a student (usually a small one). In this tutorial, our goal is to provide participants with a comprehensive understanding of the techniques and applications of KD for language models. After introducing the basic concepts including intermediate-layer matching and prediction matching, we will present advanced techniques such as reinforcement learning-based KD and multi-teacher distillation. For applications, we will focus on KD for large language models (LLMs), covering topics ranging from LLM sequence compression to LLM self-distillation. The target audience is expected to know the basics of machine learning and NLP, but do not have to be familiar with the details of math derivation and neural models"
}