Zhong, Zexuan

A Frustratingly Easy Approach for Entity and Relation Extraction

Zhong, Z., & Chen, D. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 531

Factual Probing Is [MASK]: Learning vs. Learning to Recall

Zhong, Z., Friedman, D., & Chen, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 274

Structured Pruning Learns Compact and Accurate Models

Xia, M., Zhong, Z., & Chen, D. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Advanced Neural Network ApplicationsTopic Modeling

Cited by 136

Simple Entity-Centric Questions Challenge Dense Retrievers

Sciavolino, C., Zhong, Z., Lee, J., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 98

Retrieval-based Language Models and Applications

Asai, A., Min, S., Zhong, Z., & Chen, D. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 81

Training language models with memory augmentation

Zhong, Z., Lei, T., & Chen, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 60

Training Language Models with Memory Augmentation

Zhong, Z., Lei, T., & Chen, D. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 60

MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions

Zhong, Z., Wu, Z., Manning, C. D., Potts, C., & Chen, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 55

Certifiably Robust RAG against Retrieval Corruption

Xiang, C., Wu, T., Zhong, Z., Wagner, D., Chen, D., & Mittal, P. (2024) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 7