| 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 |
Satoshi NakamuraNara Institute of Science and Technologyis.naist.jp의 이메일 확인됨