@inproceedings{barta-etal-2025-enhancing,
    title = "Enhancing {AMR} Parsing with Group Relative Policy Optimization",
    author = "Barta, Botond  and
      Hamerlik, Endre  and
      Nyist, Mil{\'a}n  and
      Ito, Masato  and
      Acs, Judit",
    editor = "Fei, Hao  and
      Tu, Kewei  and
      Zhang, Yuhui  and
      Hu, Xiang  and
      Han, Wenjuan  and
      Jia, Zixia  and
      Zheng, Zilong  and
      Cao, Yixin  and
      Zhang, Meishan  and
      Lu, Wei  and
      Siddharth, N.  and
      {\O}vrelid, Lilja  and
      Xue, Nianwen  and
      Zhang, Yue",
    booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.xllm-1.11/",
    pages = "99--105",
    ISBN = "979-8-89176-286-2",
    abstract = "We investigate the capabilities of the openly available Llama 3.2 1B language model for Abstract Meaning Representation (AMR) parsing through supervised fine-tuning, further enhanced by reinforcement learning via Group Relative Policy Optimization (GRPO). Existing supervised methods for AMR parsing face limitations due to static loss functions and challenges in capturing complex semantic phenomena. To address this, our GRPO-based approach explicitly optimizes fine-grained semantic rewards, including Smatch scores, frame-argument correctness, and structural validity of logical operations. Experimental results show that supervised fine-tuning alone establishes Llama as a capable English AMR parser, and subsequent GRPO fine-tuning further improves its performance. Our final model achieves higher Smatch scores, consistently respects critical low-level semantic constraints, and outperforms existing parsers on high-level semantic evaluation metrics across diverse linguistic phenomena."
}