Abstract
"FIE2025任务旨在使用大语言模型对文本及相关假设进行叙实性推理。我们参加了微调和非微调两个赛道,分别在人工数据集和自然数据集上采用提示词优化和词表RAG策略融合语言学知识,并利用模型集成投票方法提升判断准确率。评测结果显示,我们的方法在非微调赛道取得了0.9351的成绩,在微调赛道取得了0.9261的成绩,均位列第三名。"
- Anthology ID:
- 2025.ccl-2.19
- Volume:
- Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
- Month:
- August
- Year:
- 2025
- Address:
- Jinan, China
- Editors:
- Hongfei Lin, Bin Li, Hongye Tan
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 157–165
- Language:
- URL:
- https://aclanthology.org/2025.ccl-2.19/
- DOI:
- Bibkey:
- Cite (ACL):
- Hongyu Li, Zhihui Yang, and Renfen Hu. 2025. CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 157–165, Jinan, China. Chinese Information Processing Society of China.
- Cite (Informal):
- CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究 (Li et al., CCL 2025)
- Copy Citation:
- PDF:
- https://aclanthology.org/2025.ccl-2.19.pdf











