@inproceedings{dujingtao-2025-system,
    title = "System Report for {CCL}25-Eval Task 2 Solving Frame Semantic Parsing with {LLM}s",
    author = "Dujingtao, Dujingtao",
    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-2.8/",
    pages = "70--75",
    abstract = "``Frame Semantic Parsing (FSP) is a critical task in natural language processing (NLP) that involves identifying semantic frames, argument spans, and their corresponding roles within a sentence. This paper presents a novel approach to Chinese Frame Seman-tic Parsing by fine-tuning the Qwen3 large language model to simultaneously address three sub-tasks: Frame Identification, Argument Identification, and Role Identification.We propose a unified prompt-based framework with iterative refinements, including direct argument output for span identification and a majority-voting mechanism for frame prediction. Our experiments demonstrate significant improvements in argument and role identification through modified output formats, while frame identification benefits from ensemble voting. However, integrating Chain-of-Thought (CoT) reasoning with model-generated explanations yielded suboptimal results, suggesting limitations in the auxiliary model{'}s performance. This work highlights the potential of fine-tuned large language models for complex semantic parsing tasks and identifies avenues for further optimization.''"
}