| Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment D Jin*, Z Jin*, JT Zhou, P Szolovits Proceedings of the AAAI conference on artificial intelligence 34 (05), 8018-8025, 2020 | 1367* | 2020 |
| Deep learning for text style transfer: A survey D Jin*, Z Jin*, Z Hu, O Vechtomova, R Mihalcea Computational Linguistics 48 (1), 155-205, 2022 | 299 | 2022 |
| Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion F Schneider, O Kamal, Z Jin, B Schölkopf arXiv preprint arXiv:2301.11757, 2023 | 145 | 2023 |
| Membership Inference Attacks against Language Models via Neighbourhood Comparison J Mattern, F Mireshghallah, Z Jin, B Schölkopf, M Sachan, ... ACL 2023 Findings, 2023 | 143 | 2023 |
| GraphIE: A graph-based framework for information extraction Y Qian, E Santus, Z Jin, J Guo, R Barzilay NAACL 2019, 2019 | 141 | 2019 |
| A Taxonomy and Review of Generalization Research in NLP D Hupkes, M Giulianelli, V Dankers, M Artetxe, Y Elazar, T Pimentel, ... Nature Machine Intelligence 5 (10), 1161-1174, 2023 | 123* | 2023 |
| Can large language models infer causation from correlation? Z Jin, J Liu, Z Lyu, S Poff, M Sachan, R Mihalcea, M Diab, B Schölkopf ICLR 2024, 2024 | 101 | 2024 |
| CLadder: Assessing Causal Reasoning In Language Models Z Jin, Y Chen, F Leeb, L Gresele, O Kamal, LYU Zhiheng, K Blin, ... Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 97* | 2023 |
| When to make exceptions: Exploring language models as accounts of human moral judgment Z Jin*, S Levine*, F Gonzalez*, O Kamal, M Sap, M Sachan, R Mihalcea, ... Advances in Neural Information Processing Systems (NeurIPS) 35, 28458-28473, 2022 | 97 | 2022 |
| IMaT: Unsupervised text attribute transfer via iterative matching and translation Z Jin*, D Jin*, J Mueller, N Matthews, E Santus EMNLP-IJCNLP 2019, 2019 | 83 | 2019 |
| Logical Fallacy Detection Z Jin*, A Lalwani*, T Vaidhya, X Shen, Y Ding, Z Lyu, M Sachan, ... Findings of EMNLP 2022, 2022 | 77 | 2022 |
| Hooks in the headline: Learning to generate headlines with controlled styles D Jin, Z Jin, JT Zhou, L Orii, P Szolovits ACL 2020, 2020 | 76 | 2020 |
| CycleGT: Unsupervised graph-to-text and text-to-graph generation via cycle training Q Guo*, Z Jin*, X Qiu, W Zhang, D Wipf, Z Zhang Proceedings of the 3rd International Workshop on WebNLG at INLG 2020, 2020 | 72 | 2020 |
| Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis X Xing*, Z Jin*, D Jin, B Wang, Q Zhang, X Huang EMNLP 2020, 2020 | 61 | 2020 |
| A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models A Stolfo*, Z Jin*, K Shridhar, B Schölkopf, M Sachan ACL 2023, 2023 | 53 | 2023 |
| Genwiki: A dataset of 1.3 million content-sharing text and graphs for unsupervised graph-to-text generation Z Jin, Q Guo, X Qiu, Z Zhang Proceedings of COLING 2020, 2398-2409, 2020 | 49 | 2020 |
| Differentially private language models for secure data sharing J Mattern, Z Jin, B Weggenmann, B Schoelkopf, M Sachan EMNLP 2022, 2022 | 45 | 2022 |
| Causal direction of data collection matters: Implications of causal and anticausal learning for NLP Z Jin*, J von Kügelgen*, J Ni, T Vaidhya, A Kaushal, M Sachan, ... EMNLP 2021, 2021 | 36 | 2021 |
| Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias Y Chen, VC Raghuram, J Mattern, R Mihalcea, Z Jin Causality and Large Models@ NeurIPS 2024, 2024 | 33* | 2024 |
| 𝒫2: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation Q Guo, Z Jin, N Dai, X Qiu, X Xue, D Wipf, Z Zhang Proceedings of the 3rd International Workshop on Natural Language Generation …, 2020 | 32 | 2020 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH在 tuebingen.mpg.de 的电子邮件经过验证