@inproceedings{huang-etal-2025-mac,
    title = "{MAC}-Tuning: {LLM} Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness",
    author = "Huang, Junsheng  and
      He, Zhitao  and
      Huang, Yuchen  and
      Polisetty, Sandeep  and
      Wang, Qingyun  and
      Fung, Yi R.",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.35/",
    pages = "663--676",
    ISBN = "979-8-89176-332-6",
    abstract = "With the widespread application of large language models (LLMs), the issue of generating non-existing facts, known as hallucination, has garnered increasing attention. Previous research in enhancing LLM confidence estimation mainly focuses on the single problem setting. However, LLM awareness of its internal parameterized knowledge boundary under the more challenging multi-problem setting, which requires answering multiple problems accurately simultaneously, remains underexplored. To bridge this gap, we introduce a novel method, Multiple Answers and Confidence Stepwise Tuning (MAC-Tuning), that separates the learning of answer prediction and confidence estimation during fine-tuning on instruction data. Extensive experiments across various base models and different model sizes demonstrate that our method proposed outperforms baselines by up to 25{\%} in average precision."
}