@inproceedings{zhang-etal-2025-advancing-language,
    title = "Advancing Language Models through Instruction Tuning: Recent Progress and Challenges",
    author = "Zhang, Zhihan  and
      Lou, Renze  and
      Jiao, Fangkai  and
      Yin, Wenpeng  and
      Jiang, Meng",
    editor = "Pyatkin, Valentina  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-tutorials.2/",
    pages = "4--6",
    ISBN = "979-8-89176-336-4",
    abstract = "The capability of following instructions is a key dimension for AI systems. Therefore, in NLP, instruction tuning {--} the process of training language models to follow natural language instructions {--} has become a fundamental component of the model development pipeline. This tutorial addresses three critical questions within the field: (1) What are the current focal points in instruction tuning research? (2) What are the best practices in training an instruction-following model? (3) What new challenges have emerged? To answer these questions, the tutorial presents a systematic overview of recent advances in instruction tuning. It covers different stages in model training: supervised fine-tuning, preference optimization, and reinforcement learning. It introduces scalable strategies for building high-quality instruction data, explores approaches for training autonomous AI agents that handle complex real-world tasks, and discusses common criteria for evaluating instruction-following models. The audience will gain a comprehensive understanding of cutting-edge trends in instruction tuning and insights into promising directions for future research."
}