Li, Haoran

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing

Du, J., Wang, Y., Zhao, W., Deng, Z., Liu, S., Lou, R., Zou, H. P., Narayanan Venkit, P., Zhang, N., Srinath, M., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 23

Defense against prompt injection attack by leveraging attack techniques

Chen, Y., Li, H., Zheng, Z., Wu, D., Song, Y., & Hooi, B. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 19

PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models

Li, H., Guo, D., Li, D., Fan, W., Hu, Q., Liu, X., Chan, C., Yao, D., Yao, Y., & Song, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 16

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing

Du, J., Wang, Y., Zhao, W., Deng, Z., Liu, S., Lou, R., Zou, H. P., Venkit, P. N., Zhang, N., Srinath, M., et al. (2024) · arXiv

Topic ModelingAcademic Writing and Publishing

Cited by 2

Backdoor-powered prompt injection attacks nullify defense methods

Chen, Y., Li, H., Sui, Y., Song, Y., & Hooi, B. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 1