@inproceedings{gu-etal-2025-system,
    title = "System Report for {CCL}25-Eval Task 4: Factivity Inference Based on Dynamic Few-Shot Learning",
    author = "Gu, Sunyan  and
      Lu, Taoyu  and
      Liu, Siqi  and
      Guo, Kan  and
      Shao, Yan",
    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-2.15/",
    pages = "128--133",
    abstract = "``This paper presents the implementation approach we employ in the First Chinese Factivity Inference Evaluation 2025 (FIE2025). Factivity inference (FI) is a semantic understanding task related to judging the truth value of events, based on the use of semantic verbal elements, such as ``believe'', ``falsely claim'', ``realize''. We approach factivity inference as a large language model(LLM) based task. We aim to enhance LLM{'}s discriminative capability by adequately integrating the task-specific information via prompts, as well as constructing dynamic few-shot datasets for fine-tuning. Additionally, we incorporate data augmentation and ensemble strategies to further boost the performance. Our approach achieves a score of 93.41{\%} in the official evaluation of the shared task, ranking second in the leaderboard.''"
}