@inproceedings{wang-etal-2025-system-report,
    title = "System Report for {CCL}25-Eval Task 10: {SRAG}-{MAV} for Fine-Grained {C}hinese Hate Speech Recognition",
    author = "Wang, Jiahao  and
      Liu, Ramen  and
      Zhang, Longhui  and
      Li, Jing",
    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-2.47/",
    pages = "395--402",
    abstract = "``This paper presents our system for CCL25-Eval Task 10, addressing Fine-Grained Chinese Hate Speech Recognition (FGCHSR). We propose a novel SRAG-MAV framework that synergistically integrates task reformulation(TR), Self-Retrieval-Augmented Generation (SRAG), and Multi-Round Accumulative Voting (MAV). Our method reformulates the quadruplet extraction task into triplet extraction, uses dynamic retrieval from the training set to create contextual prompts,and applies multi-round inference with voting to improve output stability and performance. Our system, based on the Qwen2.5-7B model, achieves a Hard Score of 26.66, a Soft Score of 48.35,and an Average Score of 37.505 on the STATE ToxiCN dataset, significantly outperforming base-lines such as GPT-4o (Average Score 15.63) and fine-tuned Qwen2.5-7B (Average Score 35.365).The code is available at https://github.com/king-wang123/CCL25-SRAG-MAV.''"
}