| Incoder: A generative model for code infilling and synthesis D Fried, A Aghajanyan, J Lin, S Wang, E Wallace, F Shi, R Zhong, W Yih, ... ICLR, 2023 | 240 | 2023 |
| Visually grounded neural syntax acquisition H Shi, J Mao, K Gimpel, K Livescu ACL, 2019 | 76 | 2019 |
| 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 ICML, 2023 | 75* | 2023 |
| Language models are multilingual chain-of-thought reasoners F Shi, M Suzgun, M Freitag, X Wang, S Srivats, S Vosoughi, HW Chung, ... ICLR, 2023 | 71 | 2023 |
| Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models Y Zhang, Y Li, L Cui, D Cai, L Liu, T Fu, X Huang, E Zhao, Y Zhang, ... arXiv preprint arXiv:2309.01219, 2023 | 66 | 2023 |
| NL-Augmenter: A framework for task-sensitive natural language augmentation KD Dhole, V Gangal, S Gehrmann, A Gupta, Z Li, S Mahamood, ... NEJLT, 2023 | 47 | 2023 |
| On tree-based neural sentence modeling H Shi, H Zhou, J Chen, L Li EMNLP, 2018 | 44 | 2018 |
| Learning visually-grounded semantics from contrastive adversarial samples H Shi, J Mao, T Xiao, Y Jiang, J Sun COLING 2018, 2018 | 44 | 2018 |
| Natural Language to Code Translation with Execution F Shi, D Fried, M Ghazvininejad, L Zettlemoyer, SI Wang EMNLP, 2022 | 40 | 2022 |
| A cross-task analysis of text span representations S Toshniwal, H Shi, B Shi, L Gao, K Livescu, K Gimpel RepL4NLP, 2020 | 36 | 2020 |
| Bilingual lexicon induction via unsupervised bitext construction and word alignment H Shi, L Zettlemoyer, SI Wang ACL, 2021 | 31 | 2021 |
| Substructure substitution: Structured data augmentation for NLP H Shi, K Livescu, K Gimpel Findings of ACL, 2021 | 30 | 2021 |
| On the role of supervision in unsupervised constituency parsing H Shi, K Livescu, K Gimpel EMNLP, 2020 | 22 | 2020 |
| Cardiovascular risk prediction method based on test analysis and data mining ensemble system S Xu, H Shi, X Duan, T Zhu, P Wu, D Liu 2016 IEEE International Conference on Big Data Analysis (ICBDA), 1-5, 2016 | 11 | 2016 |
| Grammar-Based Grounded Lexicon Learning J Mao, F Shi, J Wu, R Levy, J Tenenbaum NeurIPS 34, 7865-7878, 2021 | 9 | 2021 |
| Deep Clustering of Text Representations for Supervision-Free Probing of Syntax V Gupta, H Shi, K Gimpel, M Sachan AAAI, 2022 | 7* | 2022 |
| Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing H Shi, K Gimpel, K Livescu ACL, 2022 | 6 | 2022 |
| Real multi-sense or pseudo multi-sense: An approach to improve word representation H Shi, C Li, J Hu CL4LC 2016, 2017 | 5 | 2017 |
| Constructing high quality sense-specific corpus and word embedding via unsupervised elimination of pseudo multi-sense H Shi, X Wang, Y Sun, J Hu LREC, 2018 | 3 | 2018 |
| Joint saliency estimation and matching using image regions for geo-localization of online video H Shi, J Chen, AG Hauptmann Proceedings of the 2017 ACM on International Conference on Multimedia …, 2017 | 2 | 2017 |
Kevin GimpelToyota Technological Institute at Chicago, QuillBotVerified email at ttic.edu
