@inproceedings{park-lee-2025-system,
    title = "System Report for {CCL}25-Eval Task 4: From Plain to Hierarchical {---}Knowledge-Augmented Prompting for {C}hinese Factivity Inference",
    author = "Park, Minjun  and
      Lee, Seulki",
    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-2.12/",
    pages = "105--109",
    abstract = "``To improve the factivity inference capability of large language models (LLMs), we adopted a Retrieval-Augmented Generation (RAG) framework using a curated bibliography on Chinese factivity semantics. We compared a baseline without retrieval against two RAG-based strategies, showing that hierarchical prompting with RAPTOR yields the high-est accuracy. Using recursive summarization from the bottom up, RAPTOR allows models to access document context at multiple abstraction levels, resulting in more accurate and stable inference. Our findings contribute to deeper Chinese semantic inference through linguistic knowledge-augmented prompting in factivity inference and textual entailment.''"
}