| Joint Embedding of Words and Labels for Text Classification G Wang, C Li, W Wang, Y Zhang, D Shen, X Zhang, R Henao, L Carin arXiv preprint arXiv:1805.04174, 2018 | 552 | 2018 |
| Instruction Tuning for Large Language Models: A Survey S Zhang, L Dong, X Li, S Zhang, X Sun, S Wang, J Li, R Hu, T Zhang, ... arXiv preprint arXiv:2308.10792, 2023 | 509 | 2023 |
| Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms D Shen, G Wang, W Wang, MR Min, Q Su, Y Zhang, C Li, R Henao, ... ACL, 2018 | 454 | 2018 |
| Yi: Open foundation models by 01. ai A Young, B Chen, C Li, C Huang, G Zhang, G Zhang, G Wang, H Li, J Zhu, ... arXiv preprint arXiv:2403.04652, 2024 | 343 | 2024 |
| GPT-NER: Named Entity Recognition via Large Language Models S Wang, X Sun, X Li, R Ouyang, F Wu, T Zhang, J Li, G Wang arXiv preprint arXiv:2304.10428, 2023 | 337 | 2023 |
| Text Classification via Large Language Models X Sun, X Li, J Li, F Wu, S Guo, T Zhang, G Wang arXiv preprint arXiv:2305.08377, 2023 | 213 | 2023 |
| Adversarial Text Generation via Feature-Mover's Distance L Chen, S Dai, C Tao, H Zhang, Z Gan, D Shen, Y Zhang, G Wang, ... Advances in Neural Information Processing Systems, 4666-4677, 2018 | 171 | 2018 |
| Topic-Guided Variational Autoencoders for Text Generation W Wang, Z Gan, H Xu, R Zhang, G Wang, D Shen, C Chen, L Carin arXiv preprint arXiv:1903.07137, 2019 | 159 | 2019 |
| Deconvolutional paragraph representation learning Y Zhang, D Shen, G Wang, Z Gan, R Henao, L Carin Advances in Neural Information Processing Systems, 4169-4179, 2017 | 120 | 2017 |
| POINTER: Constrained Text Generation via Insertion-based Generative Pre-training Y Zhang, G Wang, C Li, Z Gan, C Brockett, B Dolan arXiv preprint arXiv:2005.00558, 2020 | 117 | 2020 |
| Towards building the federatedGPT: Federated instruction tuning J Zhang, S Vahidian, M Kuo, C Li, R Zhang, T Yu, G Wang, Y Chen ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 111 | 2024 |
| NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing D Shen, Q Su, P Chapfuwa, W Wang, G Wang, L Carin, R Henao arXiv preprint arXiv:1805.05361, 2018 | 70 | 2018 |
| Interpreting Deep Learning Models in Natural Language Processing: A Review X Sun, D Yang, X Li, T Zhang, Y Meng, Q Han, G Wang, E Hovy, J Li arXiv preprint arXiv:2110.10470, 2021 | 65 | 2021 |
| Generative Adversarial Network Training is a Continual Learning Problem KJ Liang, C Li, G Wang, L Carin arXiv preprint arXiv:1811.11083, 2018 | 57 | 2018 |
| Methods for Numeracy-Preserving Word Embeddings D Sundararaman, S Si, V Subramanian, G Wang, D Hazarika, L Carin Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 54 | 2020 |
| An end-to-end generative architecture for paraphrase generation Q Yang, Z Huo, D Shen, Y Cheng, W Wang, G Wang, L Carin Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 47 | 2019 |
| Jointgan: Multi-domain joint distribution learning with generative adversarial nets Y Pu, S Dai, Z Gan, W Wang, G Wang, Y Zhang, R Henao, LC Duke International Conference on Machine Learning, 4151-4160, 2018 | 46 | 2018 |
| Pushing the Limits of ChatGPT on NLP Tasks X Sun, L Dong, X Li, Z Wan, S Wang, T Zhang, J Li, F Cheng, L Lyu, F Wu, ... arXiv preprint arXiv:2306.09719, 2023 | 37 | 2023 |
| Sentiment Analysis through LLM Negotiations X Sun, X Li, S Zhang, S Wang, F Wu, J Li, T Zhang, G Wang arXiv preprint arXiv:2311.01876, 2023 | 35 | 2023 |
| AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation X Xu, G Wang, YB Kim, S Lee arXiv preprint arXiv:2106.05589, 2021 | 35 | 2021 |
Jiwei LiShannon.AI, Zhejiang University, Stanford UniversityZweryfikowany adres z stanford.edu
