@inproceedings{hu-etal-2025-nile,
    title = "{NILE}: Internal Consistency Alignment in Large Language Models",
    author = "Hu, Minda  and
      Zhang, Qiyuan  and
      Wang, Yufei  and
      He, Bowei  and
      Wang, Hongru  and
      Zhou, Jingyan  and
      Li, Liangyou  and
      Wang, Yasheng  and
      Ma, Chen  and
      King, Irwin",
    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.412/",
    pages = "8140--8158",
    ISBN = "979-8-89176-332-6",
    abstract = "Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs' internal knowledge, can greatly hurt the IFT performance. However, the effective integration and balancing of the internal knowledge of LLMs, acquired during pre-training, with existing IFT datasets remains a largely underexplored area of research. To address this gap, this work introduces NILE, a novel framework to optimize the effectiveness of IFT by adjusting IFT datasets through carefully aligning the world and internal knowledge. NILE employs a three-stage pipeline to effectively quantify and adjust consistency with the internal knowledge of target LLMs. Our analysis provides compelling evidence that balancing such consistency with pre-trained internal knowledge is pivotal for unleashing LLM potential, and confirms that NILE can systematically contribute to these substantial performance improvements. Experimental results demonstrate that NILE-aligned IFT datasets sharply boost LLM performance across multiple LLM ability evaluation datasets, achieving up to 66.6{\%} gain on Arena-Hard and 68.5{\%} on Alpaca-Eval V2."
}