@inproceedings{chen-etal-2025-system,
    title = "System Report for {CCL}25-Eval Task 3: Hallucination Mitigation in {C}hinese {A}bstract {M}eaning {R}epresentation Parsing with a Multi-Agent Approach",
    author = "Chen, Rongbo  and
      Bai, Xuefeng  and
      Chen, Kehai  and
      Zhang, Min",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.9/",
    pages = "76--87",
    abstract = "``This paper introduces our system for the Fifth Chinese Abstract Meaning Representation(CAMR) Parsing Evaluation task at the 24th China National Conference on ComputationalLinguistics (CCL 2025). Our framework formulates both CAMR parsing and document-level coreference resolution as sequence-to-sequence generation tasks, employing large languagemodels (LLMs) to produce linearized CAMR sequences and coreference sequences. To mitigate hallucinations in generated graphs, we design a multi-agent system comprising: (1) two detection agents for automated error detection and hallucination identification; (2) a refinement agent that corrects graph structures based on detected inconsistencies. Experimental results show that:(1) recent LLMs, especially Qwen-3, achieve promising performance in CAMR parsing; (2)the proposed multi-agent system can effectively identify and correct hallucinations of CAMR predictions; and (3) sequence-to-sequence methods exhibit significant limitations in document-level coreference resolution due to context length constraints.''"
}