| Large language models encode clinical knowledge K Singhal, S Azizi, T Tu, SS Mahdavi, J Wei, HW Chung, N Scales, ... Nature 620 (7972), 172-180, 2023 | 2447 | 2023 |
| 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, C Cui, ... arXiv preprint arXiv:2205.10625, 2022 | 1318 | 2022 |
| Challenging big-bench tasks and whether chain-of-thought can solve them M Suzgun, N Scales, N Schärli, S Gehrmann, Y Tay, HW Chung, ... arXiv preprint arXiv:2210.09261, 2022 | 625 | 2022 |
| Teaching large language models to self-debug X Chen, M Lin, N Schärli, D Zhou arXiv preprint arXiv:2304.05128, 2023 | 576 | 2023 |
| Traits: Composable units of behaviour N Schärli, S Ducasse, O Nierstrasz, AP Black ECOOP 2003–Object-Oriented Programming: 17th European Conference, Darmstadt …, 2003 | 572 | 2003 |
| Large language models can be easily distracted by irrelevant context F Shi, X Chen, K Misra, N Scales, D Dohan, EH Chi, N Schärli, D Zhou International Conference on Machine Learning, 31210-31227, 2023 | 416 | 2023 |
| Measuring compositional generalization: A comprehensive method on realistic data D Keysers, N Schärli, N Scales, H Buisman, D Furrer, S Kashubin, ... arXiv preprint arXiv:1912.09713, 2019 | 392 | 2019 |
| Traits: A mechanism for fine-grained reuse S Ducasse, O Nierstrasz, N Schärli, R Wuyts, AP Black ACM Transactions on Programming Languages and Systems (TOPLAS) 28 (2), 331-388, 2006 | 348 | 2006 |
| H Chi, Denny Zhou, et al. 2022. Challenging big-bench tasks and whether chain-of-thought can solve them M Suzgun, N Scales, N Schärli, S Gehrmann, Y Tay, HW Chung, ... arXiv preprint arXiv:2210.09261, 2022 | 185 | 2022 |
| Compositional semantic parsing with large language models A Drozdov, N Schärli, E Akyürek, N Scales, X Song, X Chen, O Bousquet, ... arXiv preprint arXiv:2209.15003, 2022 | 148 | 2022 |
| Compositional generalization in semantic parsing: Pre-training vs. specialized architectures D Furrer, M van Zee, N Scales, N Schärli arXiv preprint arXiv:2007.08970, 2020 | 120 | 2020 |
| Applying traits to the smalltalk collection classes AP Black, N Schärli, S Ducasse ACM SIGPLAN Notices 38 (11), 47-64, 2003 | 82 | 2003 |
| Flattening traits O Nierstrasz, S Ducasse, N Schärli Universität Bern, 2005 | 54 | 2005 |
| Object-oriented encapsulation for dynamically typed languages N Schärli, AP Black, S Ducasse Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented …, 2004 | 48 | 2004 |
| Composable encapsulation policies N Schärli, S Ducasse, O Nierstrasz, R Wuyts ECOOP 2004–Object-Oriented Programming: 18th European Conference, Oslo …, 2004 | 38 | 2004 |
| Traits: Composing Classes from Behavioral Building Blocks N Schärli University of Bern, 2005 | 36 | 2005 |
| Large language models encode clinical knowledge. arXiv K Singhal, S Azizi, T Tu, SS Mahdavi, J Wei, HW Chung, N Scales, ... arXiv preprint arXiv:2212.13138, 2022 | 28 | 2022 |
| Traits: Tools and methodology AP Black, N Scharli Proceedings. 26th International Conference on Software Engineering, 676-686, 2004 | 28 | 2004 |
| Computerized systems and methods for extracting and storing information regarding entities C Semturs, L Vandevenne, D Sinopalnikov, A Lyashuk, S Steiger, ... US Patent 10,198,491, 2019 | 26 | 2019 |
| Traits: The formal model N Schärli, O Nierstrasz, S Ducasse, R Wuyts, A Black Technical Report IAM-02-006, Institut für Informatik, Universität Bern …, 2002 | 23 | 2002 |
