| 2022 |
| Emergent abilities of large language models. |
| J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, E. Chi, T. Hashimoto, O. Vinyals, P. Liang, J. Dean, and W. Fedus. |
| Least-to-most prompting enables complex reasoning in large language models. |
| D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, O. Bousquet, Q. Le, and E. Chi. |
| PaLM: Scaling language modeling with Pathways. |
| {A. Chowdhery, S. Narang, J. Devlin} and 64 additional authors. |
| Artificial stream of thought has non-trivial connections to consciousness. |
| J. Wei. |
| Self-consistency improves chain of thought reasoning in language models. |
| X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery, and D. Zhou. |
| Chain of thought prompting elicits reasoning in large language models. |
| J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou. |
| Sundar explains chain of thought prompting at Google I/O 2022 / Google AI blog |
| ACL '22 | A recipe for arbitrary text style transfer with large language models. |
| {E. Reif, D. Ippolito}, A. Yuan, A. Coenen, C. Callison-Burch, and J. Wei. |
| ICLR '22 | Finetuned language models are zero-shot learners. |
| {J. Wei, M. Bosma, V. Zhao, K. Guu}, A. Yu, B. Lester, N. Du, A. Dai, and Q. Le. |
| Google AI blog / oral |
| ICLR '22 | The MultiBERTs: BERT reproductions for robustness analysis. |
| {T. Sellam, S. Yadlowsky}, I. Tenney, J. Wei, N. Saphra, A. D'Amour, T. Linzen, J. Bastings, I. Turc, J. Eisenstein, D. Das, and E. Pavlick. |
| 2021 |
| EMNLP '21 | Frequency effects on syntactic rule learning in transformers. |
| J. Wei, D. Garrette, T. Linzen, and E. Pavlick. Google AI blog / oral |
| EMNLP '21 | Good-enough example extrapolation. |
| J. Wei. |
| ACL '21 | A cognitive regularizer for language modeling. |
| J. Wei, C. Meister, and R. Cotterell. |
| ACL '21 | Language model augmented relevance score. |
| R. Liu, J. Wei, and S. Vosoughi. |
| ACL '21 | A survey of data augmentation approaches for NLP. |
| (Findings) | {S. Feng, V. Gangal}, J. Wei, S. Chandar, S. Vosoughi, T. Mitamura, and E. Hovy. |
| ACL '21 | Modulating language models with emotions. |
| (Findings) | R. Liu, J. Wei, C. Jia, and S. Vosoughi. |
| NAACL '21 | Linguistic complexity loss in text-based therapy. |
| J. Wei, K. Finn, E. Templeton, T. Wheatley, and S. Vosoughi. |
| NAACL '21 | Few-shot text classification with triplet networks, data augmentation, and curriculum learning. |
| J. Wei, C. Huang, S. Vosoughi, Y. Cheng, and S. Xu. |
| EACL '21 | Text augmentation in a multi-task view. |
| J. Wei, C. Huang, S. Xu, and S. Vosoughi. |
| AAAI '21 | Mitigating political bias in language models through reinforced calibration (outstanding paper). |
| R. Liu, C. Jia, J. Wei, G. Xu, L. Wang, and S. Vosoughi. |
| 2019 |
| EMNLP '19 | Easy data augmentation techniques for boosting performance on text classification tasks. |
| J. Wei and K. Zou. |
