| Bertscore: Evaluating text generation with bert T Zhang, V Kishore, F Wu, KQ Weinberger, Y Artzi arXiv preprint arXiv:1904.09675, 2019 | 2856 | 2019 |
| 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 | 2567 | 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 | 1932 | 2021 |
| Stanford alpaca: An instruction-following llama model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... | 773* | 2023 |
| Revisiting few-sample BERT fine-tuning T Zhang, F Wu, A Katiyar, KQ Weinberger, Y Artzi arXiv preprint arXiv:2006.05987, 2020 | 354 | 2020 |
| 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 | 321 | 2022 |
| 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 | 186 | 2020 |
| 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 | 90 | 2019 |
| Benchmarking large language models for news summarization T Zhang, F Ladhak, E Durmus, P Liang, K McKeown, TB Hashimoto arXiv preprint arXiv:2301.13848, 2023 | 75 | 2023 |
| Alpacafarm: A simulation framework for methods that learn from human feedback Y Dubois, X Li, R Taori, T Zhang, I Gulrajani, J Ba, C Guestrin, P Liang, ... arXiv preprint arXiv:2305.14387, 2023 | 55 | 2023 |
| Evaluating verifiability in generative search engines NF Liu, T Zhang, P Liang arXiv preprint arXiv:2304.09848, 2023 | 51 | 2023 |
| 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 | 49 | 2019 |
| 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 | 42 | 2023 |
| On the inductive bias of masked language modeling: From statistical to syntactic dependencies T Zhang, T Hashimoto arXiv preprint arXiv:2104.05694, 2021 | 26 | 2021 |
| 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 | 24 | 2022 |
| 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 | 18 | 2023 |
| 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 | 9 | 2022 |
| 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 | 5 | 2023 |
| TempLM: Distilling Language Models into Template-Based Generators T Zhang, M Lee, L Li, E Shen, TB Hashimoto arXiv preprint arXiv:2205.11055, 2022 | 3 | 2022 |
| Mistral–a journey towards reproducible language model training S Karamcheti, L Orr, J Bolton, T Zhang, K Goel, A Narayan, R Bommasani, ... | 3 | 2021 |
Kilian WeinbergerCornell UniversityVerified email at cornell.edu
