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[41] Tsvetkova A. Anaphora resolution in Chinese for analysis of medical Q&A platforms. In: Proc. of the 9th CCF Int’l Conf. on Natural
Language Processing and Chinese Computing. Zhengzhou: Springer, 2020. 490–497. [doi: 10.1007/978-3-030-60457-8_40]
[42] Mihalcea R, Tarau P. TextRank: Bringing order into text. In: Proc. of the 2004 Conf. on Empirical Methods in Natural Language
Processing. Barcelona: ACL, 2004. 404–411.
[43] Wang SF, Thompson L, Iyyer M. Phrase-BERT: Improved phrase embeddings from BERT with an application to corpus exploration. In:
Proc. of the 2021 Conf. on Empirical Methods in Natural Language Processing. Punta Cana: ACL, 2021. 10837–10851. [doi: 10.18653/v1/
2021.emnlp-main.846]
[44] Khosla P, Teterwak P, Wang C, Sarna A, Tian YL, Isola P, Maschinot A, Liu C, Krishnan D. Supervised contrastive learning. In: Proc. of
the 34th Int’l Conf. on Neural Information Processing Systems. Vancouver: ACM, 2020. 1567.
[45] Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proc.
of the 2019 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Minneapolis: ACL, 2019. 4171–4186. [doi: 10.18653/v1/N19-1423]
[46] Joshi M, Chen DQ, Liu YH, Weld DS, Zettlemoyer L, Levy O. SpanBERT: Improving pre-training by representing and predicting spans.
Trans. of the Association for Computational Linguistics, 2020, 8: 64–77. [doi: 10.1162/tacl_a_00300]
[47] Strzelecki A. Is ChatGPT-like technology going to replace commercial search engines? Library Hi Tech News, 2024, 41(6): 18–21. [doi:
10.1108/LHTN-02-2024-0026]
[48] Jiang WX, Synovic N, Hyatt M, Schorlemmer TR, Sethi R, Lu YH, Thiruvathukal GK, Davis JC. An empirical study of pre-trained
model reuse in the hugging face deep learning model registry. In: Proc. of the 45th IEEE/ACM Int’l Conf. on Software Engineering
(ICSE). Melbourne: IEEE, 2023. 2463–2475. [doi: 10.1109/ICSE48619.2023.00206]
[49] He PJ, Meister C, Su ZD. Structure-invariant testing for machine translation. In: Proc. of the 42nd IEEE/ACM Int’l Conf. on Software
Engineering. Seoul: ACM, 2020. 961–973. [doi: 10.1145/3377811.3380339]
[50] Gupta S, He PJ, Meister C, Su ZD. Machine translation testing via pathological invariance. In: Proc. of the 28th ACM Joint Meeting on
European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. ACM, 2020. 863–875. [doi: 10.1145/
3368089.3409756]
[51] Hu EJ, Shen YL, Wallis P, Allen-Zhu Z, Li YZ, Wang SA, Wang L, Chen WZ. LoRA: Low-rank adaptation of large language models. In:
Proc. of the 10th Int’l Conf. on Learning Representations. OpenReview.net, 2022. 2463–2475.

附中文参考文献
[9] 钟文康, 葛季栋, 陈翔, 李传艺, 唐泽, 骆斌. 面向神经机器翻译系统的多粒度蜕变测试. 软件学报, 2021, 32(4): 1051–1066. http://www.
jos.org.cn/1000-9825/6221.htm [doi: 10.13328/j.cnki.jos.006221]

作者简介
沈庆超, 博士生, CCF 学生会员, 主要研究领域为软件测试, 深度学习编译器测试.
李行健, 硕士, 主要研究领域为深度学习, 智能测试, 多模态大模型.
姜佳君, 博士, 副教授, CCF 专业会员, 主要研究领域为软件工程, 代码调试, 程序变换.
陈俊洁, 博士, 教授, CCF 高级会员, 主要研究领域为软件分析与测试.
齐一先, 硕士生, 主要研究领域为软件分析与测试.
王赞, 博士, 教授, CCF 专业会员, 主要研究领域为基础软件测试, 智能系统测试.