@inproceedings{takahashi-etal-2025-llms,
    title = "Can {LLM}s Learn from Their Mistakes? Self-Correcting Instruction Tuning for Named Entity Recognition",
    author = "Takahashi, Takumi  and
      Taniguchi, Tomoki  and
      Zhu, Chencheng  and
      Ohkuma, Tomoko",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.106/",
    pages = "1695--1712",
    ISBN = "979-8-89176-303-6",
    abstract = "Recent instruction-tuned large language models (LLMs) have demonstrated remarkable performance on various downstream tasks, including named entity recognition (NER). However, previous approaches often generate incorrect predictions, particularly regarding entity boundaries and types. Many of these errors can be corrected to match the ground truth by revising the entity boundaries and/or types. In this paper, we propose a self-correcting instruction tuning approach that simultaneously learns to perform NER and correct errors through natural language instructions. Self-correcting instruction tuning requires only a standard annotated NER dataset. Supervision for self-correction can be automatically generated from error patterns observed in LLMs fine-tuned solely on NER tasks. We conducted extensive experiments on eight NER datasets with two LLMs to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach enhances NER performance by effectively correcting prediction errors and substantially reducing false positives. We further analyze the self-correction behavior to better understand how the models improve performance."
}