Jiang, Zhengbao

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 4,361

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 4,352

How Can We Know What Language Models Know?

Jiang, Z., Xu, F. F., Araki, J., & Neubig, G. (2020) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 1,002

Active Retrieval Augmented Generation

Jiang, Z., Xu, F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., & Neubig, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 497

GSum: A General Framework for Guided Neural Abstractive Summarization

Dou, Z.-Y., Liu, P., Hayashi, H., Jiang, Z., & Neubig, G. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 210

How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering

Jiang, Z., Araki, J., Ding, H., & Neubig, G. (2021) · Transactions of the Association for Computational Linguistics

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 177

X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models

Jiang, Z., Anastasopoulos, A., Araki, J., Ding, H., & Neubig, G. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 88

PEER: A Collaborative Language Model

Schick, T., Dwivedi-Yu, J., Jiang, Z., Petroni, F., Lewis, P., Izacard, G., You, Q., Nalmpantis, C., Grave, E., & Riedel, S. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 49

DocPrompting: Generating Code by Retrieving the Docs

Zhou, S., Alon, U., Xu, F. F., Wang, Z., Jiang, Z., & Neubig, G. (2022) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 33

Active Retrieval Augmented Generation

Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., & Neubig, G. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 18

Learning to Filter Context for Retrieval-Augmented Generation

Wang, Z., Araki, J., Jiang, Z., Parvez, M. R., & Neubig, G. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 9

Instruction-tuned Language Models are Better Knowledge Learners

Jiang, Z., Sun, Z., Shi, W., Rodriguez, P., Zhou, C., Neubig, G., Lin, X. V., Yih, W.-T., & Iyer, S. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 3