Follow
Tatsunori Hashimoto
Tatsunori Hashimoto
Other namesTatsu Hashimoto, Tatsunori B. Hashimoto
Assistant Professor, Stanford
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
30182021
Stanford alpaca: An instruction-following llama model
R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ...
1768*2023
Emergent abilities of large language models
J Wei, Y Tay, R Bommasani, C Raffel, B Zoph, S Borgeaud, D Yogatama, ...
arXiv preprint arXiv:2206.07682, 2022
16112022
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
S Sagawa, PW Koh, TB Hashimoto, P Liang
arXiv preprint arXiv:1911.08731, 2019
14682019
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
6882022
Fairness without demographics in repeated loss minimization
T Hashimoto, M Srivastava, H Namkoong, P Liang
International Conference on Machine Learning, 1929-1938, 2018
6002018
Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape
RI Sherwood, T Hashimoto, CW O'donnell, S Lewis, AA Barkal, ...
Nature biotechnology 32 (2), 171-178, 2014
4902014
Diffusion-lm improves controllable text generation
X Li, J Thickstun, I Gulrajani, PS Liang, TB Hashimoto
Advances in Neural Information Processing Systems 35, 4328-4343, 2022
4642022
Generating sentences by editing prototypes
K Guu, TB Hashimoto, Y Oren, P Liang
Transactions of the Association for Computational Linguistics 6, 437-450, 2018
3602018
Large language models can be strong differentially private learners
X Li, F Tramer, P Liang, T Hashimoto
arXiv preprint arXiv:2110.05679, 2021
2432021
Unifying human and statistical evaluation for natural language generation
TB Hashimoto, H Zhang, P Liang
arXiv preprint arXiv:1904.02792, 2019
2282019
Alpacaeval: An automatic evaluator of instruction-following models
X Li, T Zhang, Y Dubois, R Taori, I Gulrajani, C Guestrin, P Liang, ...
2222023
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
2062024
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
2042024
Whose opinions do language models reflect?
S Santurkar, E Durmus, F Ladhak, C Lee, P Liang, T Hashimoto
International Conference on Machine Learning, 29971-30004, 2023
1732023
Distributionally robust language modeling
Y Oren, S Sagawa, TB Hashimoto, P Liang
arXiv preprint arXiv:1909.02060, 2019
1692019
A retrieve-and-edit framework for predicting structured outputs
TB Hashimoto, K Guu, Y Oren, PS Liang
Advances in Neural Information Processing Systems 31, 2018
1692018
Easily accessible text-to-image generation amplifies demographic stereotypes at large scale
F Bianchi, P Kalluri, E Durmus, F Ladhak, M Cheng, D Nozza, ...
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023
1452023
Jury learning: Integrating dissenting voices into machine learning models
ML Gordon, MS Lam, JS Park, K Patel, J Hancock, T Hashimoto, ...
Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems …, 2022
1432022
The gem benchmark: Natural language generation, its evaluation and metrics
S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ...
arXiv preprint arXiv:2102.01672, 2021
1362021
The system can't perform the operation now. Try again later.
Articles 1–20