Graham Neubig

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Satoshi NakamuraNara Institute of Science and TechnologyVerified email at is.naist.jp
Sakriani SaktiProfessor, Nara Institute of Science and TechnologyVerified email at is.naist.jp
Tomoki TodaNagoya UniversityVerified email at icts.nagoya-u.ac.jp
Pengfei LiuAssociate professor at Shanghai Jiao Tong UniversityVerified email at andrew.cmu.edu
Zhengbao JiangCarnegie Mellon UniversityVerified email at cs.cmu.edu
Antonis AnastasopoulosAssistant Professor, George Mason UniversityVerified email at gmu.edu
Pengcheng YinGoogle DeepmindVerified email at google.com
Fangzheng (Frank) XuMicrosoft AIVerified email at microsoft.com
Shuyan ZhouMeta GenAI | Duke UniversityVerified email at cs.cmu.edu
Chunting ZhouFacebook AI ResearchVerified email at meta.com
Junxian HeHong Kong University of Science and TechnologyVerified email at cse.ust.hk
Xuezhe MaInformation Sciences Institute, University of Southern CaliforniaVerified email at usc.edu
Paul MichelResearch Scientist, DeepMindVerified email at deepmind.com
Junjie HuAssistant Professor, University of Wisconsin-MadisonVerified email at wisc.edu
Hiroaki HayashiSalesforce ResearchVerified email at salesforce.com
Taylor Berg-KirkpatrickUniversity of California San DiegoVerified email at ucsd.edu
Uri AlonResearch Scientist, Google DeepMindVerified email at google.com
Weizhe YuanNew York UniversityVerified email at nyu.edu
Xinyi WangResearch Scientist, Google DeepMindVerified email at google.com
Yusuke OdaNAISTVerified email at naist.ac.jp

Graham Neubig

Carnegie Mellon University, All Hands AI

Verified email at cs.cmu.edu - Homepage

Natural Language Processing Machine Learning Artificial Intelligence


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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing P Liu, W Yuan, J Fu, Z Jiang, H Hayashi, G Neubig ACM Computing Surveys, 2021	5021	2021
How can we know what language models know? Z Jiang, FF Xu, J Araki, G Neubig TACL 8, 423-438, 2020	1466	2020
NusaCrowd: Open source initiative for Indonesian NLP resources S Cahyawijaya, H Lovenia, AF Aji, G Winata, B Wilie, F Koto, R Mahendra, ... Findings of the Association for Computational Linguistics: ACL 2023, 13745-13818, 2023	1161	2023
Are Sixteen Heads Really Better than One? P Michel, O Levy, G Neubig NeurIPS 2019, 2019	1155	2019
Towards a unified view of parameter-efficient transfer learning J He, C Zhou, X Ma, T Berg-Kirkpatrick, G Neubig ICLR 2022, 2022	971	2022
XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalization J Hu, S Ruder, A Siddhant, G Neubig, O Firat, M Johnson ICML 2020, 2020	962	2020
A Syntactic Neural Model for General-Purpose Code Generation P Yin, G Neubig ACL 2017, 2017	832	2017
BARTScore: Evaluating Generated Text as Text Generation W Yuan, G Neubig, P Liu NeurIPS 2021, 2021	797	2021
PAL: Program-aided Language Models L Gao, A Madaan, S Zhou, U Alon, P Liu, Y Yang, J Callan, G Neubig ICML 2023, 2022	734	2022
A Systematic Evaluation of Large Language Models of Code FF Xu, U Alon, G Neubig, VJ Hellendoorn ICLR DL4C Workshop, 2022	697	2022
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data P Yin, G Neubig, W Yih, S Riedel ACL 2020, 2020	608	2020
Weight Poisoning Attacks on Pre-trained Models K Kurita, P Michel, G Neubig ACL 2020, 2020	460	2020
Dynet: The dynamic neural network toolkit G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ... arXiv preprint arXiv:1701.03980, 2017	460*	2017
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation? Y Qi, DS Sachan, M Felix, SJ Padmanabhan, G Neubig NAACL 2018, 2018	434	2018
Active retrieval augmented generation Z Jiang, FF Xu, L Gao, Z Sun, Q Liu, J Dwivedi-Yu, Y Yang, J Callan, ... EMNLP 2023, 2023	418	2023
Stress Test Evaluation for Natural Language Inference A Naik, A Ravichander, N Sadeh, C Rose, G Neubig COLING 2018, 2018	401	2018
Competence-based Curriculum Learning for Neural Machine Translation EA Platanios, O Stretcu, G Neubig, B Poczos, TM Mitchell NAACL 2019, 2019	395	2019
Learning to generate pseudo-code from source code using statistical machine translation (t) Y Oda, H Fudaba, G Neubig, H Hata, S Sakti, T Toda, S Nakamura ASE 2015, 574-584, 2015	384	2015
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering Z Jiang, J Araki, H Ding, G Neubig Transactions of the Association for Computational Linguistics 9, 962-977, 2021	372	2021
Pointwise prediction for robust, adaptable Japanese morphological analysis G Neubig, Y Nakata, S Mori ACL 2011, 529-533, 2011	359	2011

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