@inproceedings{zhang-etal-2025-safeconf,
    title = "{S}afe{C}onf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models",
    author = "Zhang, Bo  and
      Gao, Cong  and
      Yang, Linkang  and
      Han, Bingxu  and
      Hu, Minghao  and
      Luo, Zhunchen  and
      Geng, Guotong  and
      Bai, Xiaoying  and
      Zhang, Jun  and
      Yao, Wen  and
      Wang, Zhong",
    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/ingest-luhme/2025.findings-emnlp.186/",
    doi = "10.18653/v1/2025.findings-emnlp.186",
    pages = "3483--3495",
    ISBN = "979-8-89176-335-7",
    abstract = "Large language models (LLMs) have achieved groundbreaking progress in Natural Language Processing (NLP). Despite the numerous advantages of LLMs, they also pose significant safety risks. Self-evaluation mechanisms have gained increasing attention as a key safeguard to ensure safe and controllable content generation. However, LLMs often exhibit overconfidence, which seriously compromises the accuracy of safety self-evaluation. To address this challenge, we propose \textbf{SafeConf}, a method to enhance the safety self-evaluation capability of LLMs through confidence calibration. The method performs semantic mutations on the original safety evaluation questions and adopts a self-consistency strategy to quantify confidence based on answer accuracy on the mutated questions. Finally, these confidence scores are used to construct a dataset for fine-tuning. We conducte experiments on both Chinese and English datasets. The results show that SafeConf improves self-evaluation accuracy by an average of 5.86{\%} and 7.79{\%} over the state-of-the-art baseline methods on Qwen2.5-7B-Instruct and Llama3-8B-Instruct models, respectively, without affecting the general capabilities of the models."
}