Abstract
"本文提出了一种多智能体协同的干扰数据生成框架,旨在评测分析大语言模型在复杂干扰下的鲁棒性。该框架以数学领域为起点,逐步扩展至医学、法律、科学及通用场景,构建了涵盖拼写干扰、数字干扰、类型干扰与谣言干扰四类干扰的跨领域数据集AntIF,共计近5000条数据。在此基础上,本文对主流开源语言模型进行了系统的抗干扰能力评估,并结合不同的提示工程策略与模型微调方法,深入分析了AntIF 在提升模型鲁棒性方面的实际效果。"
- Anthology ID:
- 2025.ccl-1.26
- Volume:
- Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
- Month:
- August
- Year:
- 2025
- Address:
- Jinan, China
- Editors:
- Maosong Sun, Peiyong Duan, Zhiyuan Liu, Ruifeng Xu, Weiwei Sun
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 335–362
- Language:
- URL:
- https://aclanthology.org/2025.ccl-1.26/
- DOI:
- Bibkey:
- Cite (ACL):
- Yajing Luo, Yutao Hou, Yun Chen, and Guanhua Chen. 2025. AntIF:大语言模型抗干扰能力评估. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 335–362, Jinan, China. Chinese Information Processing Society of China.
- Cite (Informal):
- AntIF:大语言模型抗干扰能力评估 (Luo et al., CCL 2025)
- Copy Citation:
- PDF:
- https://aclanthology.org/2025.ccl-1.26.pdf










