@inproceedings{kim-etal-2025-enhancing,
    title = "Enhancing Coreference Resolution with {LLM}-driven Data Augmentation and Adversarial Filtering",
    author = "Kim, Dohyeon  and
      Jung, Gayeon  and
      Cho, Jeongseon  and
      Yang, Jihoon",
    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.56/",
    pages = "969--984",
    ISBN = "979-8-89176-303-6",
    abstract = "Coreference resolution is a fundamental task in natural language processing that involves linking different references to the same entity within a text. However, existing models often struggle to reliably identify referential relationships in contexts with extensive length or complex modifiers. This study proposes a data augmentation technique adding adjective phrases and employing a prompt-based adversarial filtering pipeline to address these challenges. Specifically, we generated and inserted contextually appropriate adjective phrases through the interaction between GPT-4o-mini based Few-shot Prompting and a Discriminative Language Model. The grammatical and semantic consistency of these phrases was validated via human evaluation and inter-annotator agreement (IAA) procedures. The generated synthetic dataset was integrated with existing data, leading to enhanced model performance. On the LitBank dataset, the CoNLL-F1 score increased by up to 1.7{\%}, while the synthetic dataset improved linguistic diversity and the complexity of referential structures. The proposed pipeline represents a significant step towards developing coreference resolution models capable of better capturing linguistic variety and demonstrating robustness under challenging conditions."
}