| Do-not-answer: A dataset for evaluating safeguards in llms Y Wang, H Li, X Han, P Nakov, T Baldwin arXiv preprint arXiv:2308.13387, 2023 | 152 | 2023 |
| 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, W Marshall, ... arXiv preprint arXiv:2308.16149, 2023 | 90 | 2023 |
| Diverse adversaries for mitigating bias in training X Han, T Baldwin, T Cohn arXiv preprint arXiv:2101.10001, 2021 | 66 | 2021 |
| Balancing out bias: Achieving fairness through balanced training X Han, T Baldwin, T Cohn arXiv preprint arXiv:2109.08253, 2021 | 59* | 2021 |
| Evaluating debiasing techniques for intersectional biases S Subramanian, X Han, T Baldwin, T Cohn, L Frermann arXiv preprint arXiv:2109.10441, 2021 | 54 | 2021 |
| Contrastive learning for fair representations A Shen, X Han, T Cohn, T Baldwin, L Frermann arXiv preprint arXiv:2109.10645, 2021 | 30 | 2021 |
| Optimising equal opportunity fairness in model training A Shen, X Han, T Cohn, T Baldwin, L Frermann arXiv preprint arXiv:2205.02393, 2022 | 25 | 2022 |
| 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 | 24* | 2022 |
| Decoupling Adversarial Training for Fair NLP X Han, T Baldwin, T Cohn Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021 | 23 | 2021 |
| Learning from failure: Integrating negative examples when fine-tuning large language models as agents R Wang, H Li, X Han, Y Zhang, T Baldwin arXiv preprint arXiv:2402.11651, 2024 | 21 | 2024 |
| 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 | 21 | 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, ... Journal of Artificial Intelligence Research 82, 687-775, 2025 | 17 | 2025 |
| Towards equal opportunity fairness through adversarial learning X Han, T Baldwin, T Cohn arXiv preprint arXiv:2203.06317, 2022 | 14 | 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 | 11 | 2024 |
| Systematic evaluation of predictive fairness X Han, A Shen, T Cohn, T Baldwin, L Frermann arXiv preprint arXiv:2210.08758, 2022 | 11 | 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 | 10 | 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 |
| 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 | 5 | 2023 |
| 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 |
| Do-not-answer: A dataset for evaluating safeguards in llms, 2023 Y Wang, H Li, X Han, P Nakov, T Baldwin URL https://arxiv. org/abs/2308.13387, 0 | 5 | |
Timothy BaldwinMBZUAI and The University of MelbourneVerified email at unimelb.edu.au
