| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 900 | 2023 |
| The Flan Collection: Designing Data and Methods for Effective Instruction Tuning S Longpre, L Hou, T Vu, A Webson, HW Chung, Y Tay, D Zhou, QV Le, ... ICML, 2023 | 405 | 2023 |
| SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer T Vu, B Lester, N Constant, R Al-Rfou, D Cer ACL, 2022 | 221 | 2022 |
| Exploring and Predicting Transferability across NLP Tasks T Vu, T Wang, T Munkhdalai, A Sordoni, A Trischler, A Mattarella-Micke, ... EMNLP, 2020 | 142 | 2020 |
| JAIST: Combining Multiple Features for Answer Selection in Community Question Answering Q Tran, V Tran, T Vu, M Nguyen, S Pham SemEval@NAACL, 2015 | 96 | 2015 |
| Gemini: A family of highly capable multimodal models R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805 1, 2023 | 92 | 2023 |
| Sentence Simplification with Memory-Augmented Neural Networks T Vu, B Hu, T Munkhdalai, H Yu NAACL, 2018 | 82 | 2018 |
| Gemini: A family of highly capable multimodal models R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805 1, 2023 | 75* | 2023 |
| FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation T Vu, M Iyyer, X Wang, N Constant, J Wei, J Wei, C Tar, YH Sung, D Zhou, ... ACL, 2024 | 60 | 2024 |
| STraTA: Self-Training with Task Augmentation for Better Few-shot Learning T Vu, MT Luong, QV Le, G Simon, M Iyyer EMNLP, 2021 | 52 | 2021 |
| Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation T Vu, A Barua, B Lester, D Cer, M Iyyer, N Constant EMNLP, 2022 | 45 | 2022 |
| Mixture-of-experts meets instruction tuning: A winning combination for large language models S Shen, L Hou, Y Zhou, N Du, S Longpre, J Wei, HW Chung, B Zoph, ... ICLR, 2024 | 35 | 2024 |
| Learning to simplify children stories with limited data T Vu, G Tran, S Pham ACIIDS, 2014 | 22 | 2014 |
| Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts S Shen, L Hou, Y Zhou, N Du, S Longpre, J Wei, HW Chung, B Zoph, ... ICLR, 2024 | 21 | 2024 |
| Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment T Vu, V Shwartz *SEM@NAACL, 2018 | 15 | 2018 |
| Self-Evaluation Improves Selective Generation in Large Language Models J Ren, Y Zhao, T Vu, PJ Liu, B Lakshminarayanan ICBINB@NeurIPS, 2023 | 11 | 2023 |
| Dialect-robust Evaluation of Generated Text J Sun, T Sellam, E Clark, T Vu, T Dozat, D Garrette, A Siddhant, ... ACL, 2023 | 11 | 2023 |
| Leveraging QA Datasets to Improve Generative Data Augmentation D Mekala, T Vu, T Schick, J Shang EMNLP, 2022 | 11 | 2022 |
| The Flan Collection: Designing Data and Methods for Effective Instruction Tuning 2023 S Longpre, L Hou, T Vu, A Webson, HW Chung, Y Tay, D Zhou, QV Le, ... arXiv preprint arXiv:2301.13688, 2023 | 4 | 2023 |
| Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification T Vu, M Iyyer ACL, 2019 | 4 | 2019 |
Denny ZhouResearch Scientist, Google DeepMindVerified email at google.com
