| Gpteval: Nlg evaluation using gpt-4 with better human alignment Y Liu, D Iter, Y Xu, S Wang, R Xu, C Zhu arXiv preprint arXiv:2303.16634, 2023 | 390 | 2023 |
| Identifying content for planned events across social media sites H Becker, D Iter, M Naaman, L Gravano Proceedings of the fifth ACM international conference on Web search and data …, 2012 | 229 | 2012 |
| From laptop to lambda: Outsourcing everyday jobs to thousands of transient functional containers S Fouladi, F Romero, D Iter, Q Li, S Chatterjee, C Kozyrakis, M Zaharia, ... 2019 USENIX annual technical conference (USENIX ATC 19), 475-488, 2019 | 226 | 2019 |
| Generate rather than retrieve: Large language models are strong context generators W Yu, D Iter, S Wang, Y Xu, M Ju, S Sanyal, C Zhu, M Zeng, M Jiang arXiv preprint arXiv:2209.10063, 2022 | 176 | 2022 |
| Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia D Iter, JH Yoon, D Jurafsky Workshop on Computational Linguistics and Clinical Psychology, 136–146, 2018 | 119 | 2018 |
| Automatic prompt optimization with" gradient descent" and beam search R Pryzant, D Iter, J Li, YT Lee, C Zhu, M Zeng arXiv preprint arXiv:2305.03495, 2023 | 95 | 2023 |
| Pretraining with contrastive sentence objectives improves discourse performance of language models D Iter, K Guu, L Lansing, D Jurafsky arXiv preprint arXiv:2005.10389, 2020 | 81 | 2020 |
| Omnivore: An optimizer for multi-device deep learning on cpus and gpus S Hadjis, C Zhang, I Mitliagkas, D Iter, C Ré arXiv preprint arXiv:1606.04487, 2016 | 77 | 2016 |
| Automatic identification and presentation of twitter content for planned events H Becker, F Chen, D Iter, M Naaman, L Gravano Proceedings of the International AAAI Conference on Web and Social Media 5 …, 2011 | 66 | 2011 |
| Generating adversarial examples for speech recognition D Iter, J Huang, M Jermann Stanford Technical Report, 2017 | 57 | 2017 |
| G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment (2023) Y Liu, D Iter, Y Xu, S Wang, R Xu, C Zhu URL http://arxiv. org/abs/2303.16634, 0 | 48 | |
| Learnable structured clustering framework for deep metric learning HO Song, S Jegelka, V Rathod, K Murphy arXiv preprint arXiv:1612.01213 1 (2), 8, 2016 | 32 | 2016 |
| Flipper: A systematic approach to debugging training sets P Varma, D Iter, C De Sa, C Ré Proceedings of the 2nd Workshop on Human-in-the-Loop Data Analytics, 1-5, 2017 | 28 | 2017 |
| The trade-offs of domain adaptation for neural language models D Grangier, D Iter arXiv preprint arXiv:2109.10274, 2021 | 24 | 2021 |
| Socratic learning: Augmenting generative models to incorporate latent subsets in training data P Varma, B He, D Iter, P Xu, R Yu, C De Sa, C Ré arXiv preprint arXiv:1610.08123, 2016 | 24 | 2016 |
| Phi-3 technical report: A highly capable language model locally on your phone M Abdin, SA Jacobs, AA Awan, J Aneja, A Awadallah, H Awadalla, ... arXiv preprint arXiv:2404.14219, 2024 | 20 | 2024 |
| Socratic learning: Correcting misspecified generative models using discriminative models P Varma, B He, D Iter, P Xu, R Yu, C De Sa, C Ré arXiv preprint arXiv:1610.08123, 2017 | 13 | 2017 |
| Target tracking with kalman filtering, knn and lstms D Iter, J Kuck, P Zhuang, CM Learning CS229: Machine Learning, Stanford University, 2016 | 13 | 2016 |
| Focus on what matters: Applying discourse coherence theory to cross document coreference W Held, D Iter, D Jurafsky arXiv preprint arXiv:2110.05362, 2021 | 12 | 2021 |
| How does in-context learning help prompt tuning? S Sun, Y Liu, D Iter, C Zhu, M Iyyer arXiv preprint arXiv:2302.11521, 2023 | 11 | 2023 |
