| Diverse adversaries for mitigating bias in training X Han, T Baldwin, T Cohn arXiv preprint arXiv:2101.10001, 2021 | 49 | 2021 |
| Evaluating debiasing techniques for intersectional biases S Subramanian, X Han, T Baldwin, T Cohn, L Frermann arXiv preprint arXiv:2109.10441, 2021 | 44 | 2021 |
| Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models N Sengupta, SK Sahu, B Jia, S Katipomu, H Li, F Koto, OM Afzal, ... arXiv preprint arXiv:2308.16149, 2023 | 42 | 2023 |
| Balancing out bias: Achieving fairness through balanced training X Han, T Baldwin, T Cohn arXiv preprint arXiv:2109.08253, 2021 | 39* | 2021 |
| Do-not-answer: Evaluating safeguards in LLMs Y Wang, H Li, X Han, P Nakov, T Baldwin Findings of the Association for Computational Linguistics: EACL 2024, 896-911, 2024 | 37* | 2024 |
| Contrastive learning for fair representations A Shen, X Han, T Cohn, T Baldwin, L Frermann arXiv preprint arXiv:2109.10645, 2021 | 24 | 2021 |
| Decoupling Adversarial Training for Fair NLP X Han, T Baldwin, T Cohn Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021 | 20 | 2021 |
| Fairlib: A unified framework for assessing and improving fairness X Han, A Shen, Y Li, L Frermann, T Baldwin, T Cohn Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 16* | 2022 |
| Optimising equal opportunity fairness in model training A Shen, X Han, T Cohn, T Baldwin, L Frermann arXiv preprint arXiv:2205.02393, 2022 | 16 | 2022 |
| Does Representational Fairness Imply Empirical Fairness? A Shen, X Han, T Cohn, T Baldwin, L Frermann Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022 …, 2022 | 14 | 2022 |
| Towards equal opportunity fairness through adversarial learning X Han, T Baldwin, T Cohn arXiv preprint arXiv:2203.06317, 2022 | 11 | 2022 |
| Systematic evaluation of predictive fairness X Han, A Shen, T Cohn, T Baldwin, L Frermann arXiv preprint arXiv:2210.08758, 2022 | 9 | 2022 |
| Fair enough: Standardizing evaluation and model selection for fairness research in NLP X Han, T Baldwin, T Cohn arXiv preprint arXiv:2302.05711, 2023 | 7 | 2023 |
| Grounding learning of modifier dynamics: An application to color naming X Han, P Schulz, T Cohn arXiv preprint arXiv:1909.07586, 2019 | 6 | 2019 |
| Commodity recommendation for users based on E-commerce data F Yang, X Han, J Lang, W Lu, L Liu, L Zhang, J Pan Proceedings of the 2nd International Conference on Big Data Research, 146-149, 2018 | 5 | 2018 |
| Everybody needs good neighbours: An unsupervised locality-based method for bias mitigation X Han, T Baldwin, T Cohn The Eleventh International Conference on Learning Representations, 2022 | 3 | 2022 |
| Against The Achilles' Heel: A Survey on Red Teaming for Generative Models L Lin, H Mu, Z Zhai, M Wang, Y Wang, R Wang, J Gao, Y Zhang, W Che, ... arXiv preprint arXiv:2404.00629, 2024 | 2 | 2024 |
| Towards fair dataset distillation for text classification X Han, A Shen, Y Li, L Frermann, T Baldwin, T Cohn Proceedings of The Third Workshop on Simple and Efficient Natural Language …, 2022 | 2 | 2022 |
| A Chinese Dataset for Evaluating the Safeguards in Large Language Models Y Wang, Z Zhai, H Li, X Han, L Lin, Z Zhang, J Zhao, P Nakov, T Baldwin arXiv preprint arXiv:2402.12193, 2024 | 1 | 2024 |
| Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability? G Kuzmin, A Vazhentsev, A Shelmanov, X Han, S Suster, M Panov, ... Proceedings of the 13th International Joint Conference on Natural Language …, 2023 | 1 | 2023 |
Timothy BaldwinMBZUAI and The University of Melbourne在 unimelb.edu.au 的电子邮件经过验证
