- Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Yichen Wang, Chen Liu, Yu Lan, Chao Shen. Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language Models, NeurIPS 2024. (人工智能顶会，CCF A)
- Xiaoming Liu, Chen Liu, Zhaohan Zhang, Chengzhengxu Li, Longtian Wang, Yu Lan, Chao Shen. StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation, EMNLP 2024.(自然语言处理顶会，CCF B)
- Wang, Yichen and Feng, Shangbin and Hou, Abe Bohan and Pu, Xiao and Shen, Chao and Liu, Xiaoming and Tsvetkov, Yulia and He, Tianxing. (2024). Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks. ACL 2024. （自然语言处理顶会，CCF A）
- Liu, S., Liu, X*., Wang, Y., Cheng, Z., Li, C., Zhang, Z., ... & Shen, C. (2024). Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better. ACL 2024. （自然语言处理顶会，CCF A）
- Li, Chengzhengxu, Xiaoming Liu*, Yichen Wang, Duyi Li, Yu Lan, and Chao Shen. "Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Optimization for Few-shot Learning." AAAI 2024 （CCF A，Acceptance rate 23.75%) [Paper] [Codes]
- Yichen Wang, Kevin Yang, Xiaoming Liu, Dan Klein. Improving Pacing in Long-Form Story Planning. EMNLP 2023 Findings. （自然语言处理顶会，CCF B)）[Paper] [Codes]
- Xiaoming Liu*, Zhaohan Zhang*, Yichen Wang*, Hang Pu, Yu Lan, and Chao Shen. CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning. EMNLP 2023 Long Paper.(* All the authors contributed equally to this work，自然语言处理顶会，CCF B, Acceptance rate 21.3%) [Paper] [Codes] [Data]
- Xiaoming Liu, Shaocong Wu, Zhaohan Zhang and Chao Shen, Unify Local and Global Information for Top-N Recommendation, In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1262-1272. 2022. (CCF A, Full Paper, Accepted rate 161/794=20%)[Paper] [Codes]
- Li, Q.*, Liu, X.*, Shen, C., Peng, X., Zhou, Y. and Guan, X., 2020. Learning Graph Embedding with Limited Labeled Data: An Efficient Sampling Approach. arXiv preprint arXiv:2003.06100. (* Both authors contributed equally to this work)
- Liu, X., Shen, C., Fan, Y., Liu, X., Zhou, Y., & Guan, X. (2018, December). A Co-Evolutionary Model for Inferring Online Social Network User Behaviors. In 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) (pp. 85-90). IEEE. (Best Student Paper Award)
- Liu, X., Zhou, Y., Hu, C., Guan, X., & Leng, J. (2014, April). Detecting community structure for undirected big graphs based on random walks. In Proceedings of the 23rd International Conference on World Wide Web (pp. 1151-1156). (CCF A, Workshop on Big Graph Minng)
- Liu, X., Zhou, Y., Hu, C., Guan, X., & Sun, X. (2015, July). A feasible graph partition framework for random walks implemented by parallel computing in big graph. In 2015 34th Chinese Control Conference (CCC) (pp. 4986-4991). IEEE.
