@inproceedings{liang-etal-2025-fine,
    title = "Fine-Grained Constraint Generation-Verification for Improved Instruction-Following",
    author = "Liang, Zhixiang  and
      Hou, Zhenyu  and
      Wang, Xiao",
    editor = "Dhole, Kaustubh  and
      Clinciu, Miruna",
    booktitle = "Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM{\texttwosuperior})",
    month = jul,
    year = "2025",
    address = "Vienna, Austria and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.gem-1.71/",
    pages = "862--879",
    ISBN = "979-8-89176-261-9",
    abstract = "The ability of Large Language Models (LLMs) to follow natural language instructions is crucial. However, numerous studies have demonstrated that LLMs still struggle to follow instructions with complex constraints, limiting their application in other areas. Meanwhile, obtaining high-quality instruction-following data often requires substantial manual annotation, which is both time-consuming and labor-intensive. In this work, we present FiGV, a fine-grained constraint generation-verification strategy for synthesizing instruction-following data. FiGV employs LLM-driven processes to generate fine-grained constraints and check the legality of the synthetic instructions. Subsequently, LLMs are utilized to perform nuanced, constraint-level verification to determine whether the generated responses adhere to the synthetic instructions, with LLM-generated functions incorporated for auxiliary validation tailored to the types of constraints. Experiments on 7B to 70B models demonstrate that FiGV consistently achieves strong performance across various benchmarks designed to evaluate the instruction-following capabilities of LLMs."
}