@inproceedings{wu-etal-2025-system,
    title = "System Report for {CCL}25-Eval Task 10: Prompt-Driven Large Language Model Merge for Fine-Grained {C}hinese Hate Speech Detection",
    author = "Wu, Binglin  and
      Zou, Jiaxiu  and
      Li, Xianneng",
    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-2.48/",
    pages = "403--410",
    abstract = "``The proliferation of hate speech on Chinese social media poses urgent societal risks, yet traditional systems struggle to decode context-dependent rhetorical strategies and evolving slang. To bridge this gap, we propose a novel three-stage LLM-based framework: Prompt Engineering, Supervised Fine-tuning, and LLM Merging. First, context-aware prompts are designed to guide LLMs in extracting implicit hate patterns. Next, task-specific features are integrated during supervised fine-tuning to enhance domain adaptation. Finally, merging fine-tuned LLMs improves robustness against out-of-distribution cases. Evaluations on the STATE-ToxiCN benchmark validate the framework{'}s effectiveness, demonstrating superior performance over baseline methods in detecting fine-grained hate speech.''"
}