| Bertscore: Evaluating text generation with bert T Zhang, V Kishore, F Wu, KQ Weinberger, Y Artzi arXiv preprint arXiv:1904.09675, 2019 | 5230 | 2019 |
| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4190 | 2021 |
| Simplifying Graph Convolutional Networks F Wu, T Zhang, AH Souza Jr, C Fifty, T Yu, KQ Weinberger Proceedings of the 36th International Conference on Machine Learning, 2019 | 3660 | 2019 |
| Stanford alpaca: An instruction-following llama model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... | 2516* | 2023 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 1064 | 2022 |
| Revisiting few-sample BERT fine-tuning T Zhang, F Wu, A Katiyar, KQ Weinberger, Y Artzi arXiv preprint arXiv:2006.05987, 2020 | 449 | 2020 |
| Alpacaeval: An automatic evaluator of instruction-following models X Li, T Zhang, Y Dubois, R Taori, I Gulrajani, C Guestrin, P Liang, ... | 441 | 2023 |
| Benchmarking large language models for news summarization T Zhang, F Ladhak, E Durmus, P Liang, K McKeown, TB Hashimoto Transactions of the Association for Computational Linguistics 12, 39-57, 2024 | 389 | 2024 |
| Alpacafarm: A simulation framework for methods that learn from human feedback Y Dubois, CX Li, R Taori, T Zhang, I Gulrajani, J Ba, C Guestrin, PS Liang, ... Advances in Neural Information Processing Systems 36, 2024 | 372 | 2024 |
| Identifying mislabeled data using the area under the margin ranking G Pleiss, T Zhang, E Elenberg, KQ Weinberger Advances in Neural Information Processing Systems 33, 17044-17056, 2020 | 268 | 2020 |
| DS-1000: A natural and reliable benchmark for data science code generation Y Lai, C Li, Y Wang, T Zhang, R Zhong, L Zettlemoyer, W Yih, D Fried, ... International Conference on Machine Learning, 18319-18345, 2023 | 194 | 2023 |
| Evaluating verifiability in generative search engines NF Liu, T Zhang, P Liang arXiv preprint arXiv:2304.09848, 2023 | 178 | 2023 |
| Stanford alpaca: an instruction-following llama model (2023) R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... URL https://github. com/tatsu-lab/stanford_alpaca 1 (9), 2023 | 115 | 2023 |
| SWALP: Stochastic Weight Averaging in Low-Precision Training G Yang, T Zhang, P Kirichenko, J Bai, AG Wilson, C De Sa Proceedings of the 36th International Conference on Machine Learning, 2019 | 109 | 2019 |
| Decentralized training of foundation models in heterogeneous environments B Yuan, Y He, J Davis, T Zhang, T Dao, B Chen, PS Liang, C Re, C Zhang Advances in Neural Information Processing Systems 35, 25464-25477, 2022 | 82 | 2022 |
| Qpytorch: A low-precision arithmetic simulation framework T Zhang, Z Lin, G Yang, C De Sa 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive …, 2019 | 68 | 2019 |
| Coder reviewer reranking for code generation T Zhang, T Yu, T Hashimoto, M Lewis, W Yih, D Fried, S Wang International Conference on Machine Learning, 41832-41846, 2023 | 67 | 2023 |
| When do pre-training biases propagate to downstream tasks? a case study in text summarization F Ladhak, E Durmus, M Suzgun, T Zhang, D Jurafsky, K McKeown, ... Proceedings of the 17th Conference of the European Chapter of the …, 2023 | 46 | 2023 |
| On the inductive bias of masked language modeling: From statistical to syntactic dependencies T Zhang, T Hashimoto arXiv preprint arXiv:2104.05694, 2021 | 32 | 2021 |
| LADIS: Language disentanglement for 3D shape editing I Huang, P Achlioptas, T Zhang, S Tulyakov, M Sung, L Guibas arXiv preprint arXiv:2212.05011, 2022 | 11 | 2022 |
Kilian WeinbergerCornell UniversityVerified email at cornell.edu