Neubig, Graham

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

A Syntactic Neural Model for General-Purpose Code Generation

Yin, P., & Neubig, G. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Software Engineering ResearchNatural Language Processing Techniques

Cited by 623

A Syntactic Neural Model for General-Purpose Code Generation

Yin, P., & Neubig, G. (2017) · arXiv

Software Engineering ResearchNatural Language Processing Techniques

Cited by 585

A systematic evaluation of large language models of code

Xu, F. F., Alon, U., Neubig, G., & Hellendoorn, V. J. (2022) · Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming

Software Engineering ResearchTopic Modeling

Cited by 540

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

TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Yin, P., Neubig, G., Yih, W.-T., & Riedel, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 487

Are Sixteen Heads Really Better than One?

Michel, P., Levy, O., & Neubig, G. (2019) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 404

Weight Poisoning Attacks on Pretrained Models

Kurita, K., Michel, P., & Neubig, G. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 354

BARTScore: Evaluating Generated Text as Text Generation

Yuan, W., Neubig, G., & Liu, P. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 353

Controlling Output Length in Neural Encoder-Decoders

Kikuchi, Y., Neubig, G., Sasano, R., Takamura, H., & Okumura, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 222

Learning to mine aligned code and natural language pairs from stack overflow

Yin, P., Deng, B., Chen, E., Vasilescu, B., & Neubig, G. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories

Software Engineering ResearchNatural Language Processing Techniques

Cited by 218

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

Learning to Deceive with Attention-Based Explanations

Pruthi, D., Gupta, M., Dhingra, B., Neubig, G., & Lipton, Z. C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 203

TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

Yin, P., & Neubig, G. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 203

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

PAL: Program-aided Language Models

Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 155

In-IDE Code Generation from Natural Language: Promise and Challenges

Xu, F. F., Vasilescu, B., & Neubig, G. (2022) · ACM Transactions on Software Engineering and Methodology

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 154

In-IDE Code Generation from Natural Language: Promise and Challenges

Xu, F. F., Vasilescu, B., & Neubig, G. (2021) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 153

Re-evaluating Evaluation in Text Summarization

Bhandari, M., Gour, P. N., Ashfaq, A., Liu, P., & Neubig, G. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Advanced Text Analysis TechniquesTopic Modeling

Cited by 148

Learning Performance-Improving Code Edits

Shypula, A., Madaan, A., Zeng, Y., Alon, U., Gardner, J., Hashemi, M., Neubig, G., Ranganathan, P., Bastani, O., & Yazdanbakhsh, A. (2023) · arXiv

Software Engineering ResearchParallel Computing and Optimization Techniques

Cited by 146

Detecting Hallucinated Content in Conditional Neural Sequence Generation

Zhou, C., Neubig, G., Gu, J., Diab, M., Guzmán, F., Zettlemoyer, L., & Ghazvininejad, M. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Natural Language Processing TechniquesTopic Modeling

Cited by 122

CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code

Zhou, S., Alon, U., Agarwal, S., & Neubig, G. (2023) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 100