Page 215 - 《软件学报》2025年第12期
P. 215
5596 软件学报 2025 年第 36 卷第 12 期
Computational Linguistics, 2023. 10572–10601. [doi: 10.18653/v1/2023.findings-emnlp.710]
[17] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proc. of the
34th Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 6000–6010.
[18] Wang NY, Ye YX, Liu L, Feng LZ, Bao T, Peng T. Language models based on deep learning: A review. Ruan Jian Xue Bao/Journal of
Software, 2021, 32(4): 1082–1115 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6169.htm [doi: 10.13328/j.cnki.
jos.006169]
[19] Li ZY, Zhu HX, Lu ZR, Yin M. Synthetic data generation with large language models for text classification: Potential and limitations. In:
Proc. of the 2023 Conf. on Empirical Methods in Natural Language Processing. Singapore: Association for Computational Linguistics,
2023. 10443–10461. [doi: 10.18653/v1/2023.emnlp-main.647]
[20] Li G, Peng X, Wang QX, Xie T, Jin Z, Wang J, Ma XX, Li XD. Challenges from LLMs as a natural language based human-machine
collaborative tool for software development and evolution. Ruan Jian Xue Bao/Journal of Software, 2023, 34(10): 4601–4606 (in Chinese
with English abstract). http://www.jos.org.cn/1000-9825/7008.htm [doi: 10.13328/j.cnki.jos.007008]
[21] Zhao ZH, Fan WQ, Li JT, Liu YQ, Mei XW, Wang YQ, Wen Z, Wang F, Zhao XY, Tang JL, Li Q. Recommender systems in the era of
large language models (LLMs). IEEE Trans. on Knowledge and Data Engineering, 2024, 36(11): 6889–6907. [doi: 10.1109/TKDE.2024.
3392335]
[22] 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: Association for Computational Linguistics, 2019. 4171–4186. [doi: 10.18653/v1/N19-1423]
[23] Li SC, Wang ZQ, Zhou GD. LLM enhanced cross domain aspect-based sentiment analysis. Ruan Jian Xue Bao/Journal of Software,
2025, 36(2): 644–659 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/7156.htm [doi: 10.13328/j.cnki.jos.007156]
[24] Liang Z, Wang HZ, Dai JJ, Shao XY, Ding XO, Mu TY. Interpretability of entity matching based on pre-trained language model. Ruan
Jian Xue Bao/Journal of Software, 2023, 34(3): 1087–1108 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6794.
htm [doi: 10.13328/j.cnki.jos.006794]
[25] Ju SG, Huang FY, Sun JP. Idiom cloze algorithm integrating with pre-trained language model. Ruan Jian Xue Bao/Journal of Software,
2022, 33(10): 3793–3805 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6307.htm [doi: 10.13328/j.cnki.jos.
006307]
[26] Shi D, You CB, Huang JT, Li TH, Xiong DY. CORECODE: A common sense annotated dialogue dataset with benchmark tasks for
Chinese large language models. In: Proc. of the 38th AAAI Conf. on Artificial Intelligence. Vancouver: AAAI Press, 2024. 18952–18960.
[doi: 10.1609/aaai.v38i17.29861]
[27] Xu P, Patwary M, Shoeybi M, Puri R, Fung P, Anandkumar A, Catanzaro B. MEGATRON-CNTRL: Controllable story generation with
external knowledge using large-scale language models. In: Proc. of the 2020 Conf. on Empirical Methods in Natural Language
Processing. Pennsylvania: Association for Computational Linguistics, 2020. 2831–2845. [doi: 10.18653/v1/2020.emnlp-main.226]
[28] Verma G, Rossi R, Tensmeyer C, Gu JX, Nenkova A. Learning the visualness of text using large vision-language models. In: Proc. of the
2023 Conf. on Empirical Methods in Natural Language Processing. Singapore: Association for Computational Linguistics, 2023.
2394–2408. [doi: 10.18653/v1/2023.emnlp-main.147]
[29] Wu TY, He SZ, Liu JP, Sun SQ, Liu K, Han QL, Tang Y. A brief overview of ChatGPT: The history, status quo and potential future
development. IEEE/CAA Journal of Automatica Sinica, 2023, 10(5): 1122–1136. [doi: 10.1109/JAS.2023.123618]
[30] Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, Schulman J, Hilton J, Kelton
F, Miller L, Simens M, Askell A, Welinder P, Christiano P, Leike J, Lowe R. Training language models to follow instructions with
human feedback. In: Proc. of the 2022 Annual Conf. on Neural Information Processing Systems. New Orleans: Curran Associates Inc.,
2022. 27730–27744.
[31] Chowdhery A, Narang S, Devlin J, et al. PaLM: Scaling language modeling with pathways. Journal of Machine Learning Research, 2023,
24(240): 1–113.
[32] Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou YQ, Li W, Liu PJ. Exploring the limits of transfer learning with a
unified text-to-text transformer. The Journal of Machine Learning Research, 2020, 21(140): 1–67.
[33] Li S, Chen JJ, Yuan SY, Wu XY, Yang H, Tao SM, Xiao YH. Translate meanings, not just words: IdiomKB’s role in optimizing
idiomatic translation with language models. In: Proc. of the 38th AAAI Conf. on Artificial Intelligence. Vancouver: AAAI Press, 2024.
18554–18563. [doi: 10.1609/aaai.v38i17.29817]
[34] Du ZX, Qian YJ, Liu X, Ding M, Qiu JZ, Yang ZL, Tang J. GLM: General language model pretraining with autoregressive blank
infilling. In: Proc. of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin: Association for Computational

