@inproceedings{bao-etal-2025-permitted,
    title = "Permitted Knowledge Boundary: Evaluating the Knowledge-Constrained Responsiveness of Large Language Models",
    author = "Bao, Wenrui  and
      Wang, Kai  and
      Luo, Siqiang  and
      Li, Xiang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.722/",
    doi = "10.18653/v1/2025.findings-emnlp.722",
    pages = "13390--13405",
    ISBN = "979-8-89176-335-7",
    abstract = "With the advancement of large language models (LLMs), recent research has raised concerns about their controllability.. In this paper, we argue for the importance of Knowledge-Constrained Responsiveness (KCR), ensuring that LLMs comply with human-defined constraints. However, KCR is an implicit and unobservable capability of LLMs, functioning as a black box that currently eludes quantitative assessment. To address this issue, we first introduce the definition of ``permitted boundary'' and define the ``boundary bias'' to depict KCR. We propose six metrics to quantify the boundary bias of LLMs and subsequently assess the KCR. Furthermore, we establish a benchmark with two new datasets, KCR-SimpleQA and KCR-WebNLG, to evaluate the performance of LLMs. Our extensive experiments show that several tested LLMs still struggle to varying degrees when adhering to constraints, especially without the corresponding knowledge."
}