@inproceedings{kang-etal-2026-wsdpo,
    title = "{WSDPO}: A Generative Word Sense Disambiguation Framework with Chain-of-Thought and Preference Optimization",
    author = "Kang, Kunpeng  and
      Li, Shuaimin  and
      Zhang, Kaiyuan  and
      Zhang, Luyang  and
      Si, Jiasheng  and
      Xu, Bing  and
      Chen, Kehai  and
      Yang, Muyun  and
      Lu, Wenpeng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1610/",
    pages = "34870--34889",
    ISBN = "979-8-89176-390-6",
    abstract = "Word sense disambiguation (WSD) is a foundational task in natural language processing. Recent research has reformulated WSD for large language models (LLMs) as a generative task, where the model produces a definition to convey the intended meaning of an ambiguous word in context.In practice, most existing approaches implement this formulation through straightforward supervised fine-tuning, which tends to prioritize superficial context-to-gloss memorization over true contextual sense discrimination, leading to degraded performance on less frequent senses (LFS), particularly in unseen settings.To address this issue, we propose WSDPO, a training framework for generative WSD with chain-of-thought (CoT) and preference optimization. WSDPO consists of three stages: (1) disambiguation-aware CoT construction, which produces training data containing explicit disambiguation steps for the later stage;(2) disambiguation-guided supervised fine-tuning, which explicitly trains the model to discriminate word sense before generating the final definition; and(3) preference-based optimization, which further strengthens the model{'}s ability to generate sense-faithful definitions by optimizing it using preference pairs constructed from multiple sampled CoT outputs.Extensive experiments across benchmark datasets and multiple backbone LLMs demonstrate that WSDPO achieves substantial performance gains on rare and unseen settings, and exhibits strong generalization in standard evaluation settings."
}