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        <title>System Report for CCL25-Eval Task 4: Factivity Inference Based on Dynamic Few-Shot Learning</title>
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    <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.”</abstract>
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%0 Conference Proceedings
%T System Report for CCL25-Eval Task 4: Factivity Inference Based on Dynamic Few-Shot Learning
%A Gu, Sunyan
%A Lu, Taoyu
%A Liu, Siqi
%A Guo, Kan
%A Shao, Yan
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F gu-etal-2025-system
%X “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.”
%U https://aclanthology.org/2025.ccl-2.15/
%P 128-133

Markdown (Informal)

[System Report for CCL25-Eval Task 4: Factivity Inference Based on Dynamic Few-Shot Learning](https://aclanthology.org/2025.ccl-2.15/) (Gu et al., CCL 2025)

• System Report for CCL25-Eval Task 4: Factivity Inference Based on Dynamic Few-Shot Learning (Gu et al., CCL 2025)

ACL

• Sunyan Gu, Taoyu Lu, Siqi Liu, Kan Guo, and Yan Shao. 2025. System Report for CCL25-Eval Task 4: Factivity Inference Based on Dynamic Few-Shot Learning. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 128–133, Jinan, China. Chinese Information Processing Society of China.