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Pretrained language models (PLMs), such as GPT2, have achieved remarkable empirical performance in text generation tasks.
Generalized rejection sampling schemes and applications in signal processing
L. Martino and J. Míguez · 2010
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Pointer sentinel mixture models
S. Merity, C. Xiong, J. Bradbury, and R. Socher · 2016
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Mutual information neural estimation
M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm · 2018
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Learning gender-neutral word embeddings
J. Zhao, Y. Zhou, Z. Li, W. Wang, and K.-W. Chang · 2018
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Evaluating the underlying gender bias in contextualized word embeddings
C. Basta, M. R. Costa-Jussà, and N. Casas · 2019
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Plug and play language models: A simple approach to controlled text generation
S. Dathathri, A. Madotto, J. Lan, J. Hung, E. Frank, P. Molino, J. Yosinski, and R. Liu · 2019
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Queens are powerful too: Mitigating gender bias in dialogue generation
E. Dinan, A. Fan, A. Williams, J. Urbanek, D. Kiela, and J. Weston · 2019
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Measuring bias in contextualized word representations
K. Kurita, N. Vyas, A. Pareek, A. W. Black, and Y. Tsvetkov · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al · 2019
Cited alongside, same era.
The woman worked as a babysitter: On biases in language generation
E. Sheng, K.-W. Chang, P. Natarajan, and N. Peng · 2019
Cited alongside, same era.
Plan-and-write: Towards better automatic storytelling
L. Yao, N. Peng, R. Weischedel, K. Knight, D. Zhao, and R. Yan · 2019
Cited alongside, same era.
Gender bias in contextualized word embeddings
J. Zhao, T. Wang, M. Yatskar, R. Cotterell, V. Ordonez, and K.-W. Chang · 2019
Cited alongside, same era.
Fairfil: Contrastive neural debiasing method for pretrained text encoders
P. Cheng, W. Hao, S. Yuan, S. Si, and L. Carin · 2021
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Bold: Dataset and metrics for measuring biases in open-ended language generation
J. Dhamala, T. Sun, V. Kumar, S. Krishna, Y. Pruksachatkun, K.-W. Chang, and R. Gupta · 2021
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A survey of natural language generation
C. Dong, Y. Li, H. Gong, M. Chen, J. Li, Y. Shen, and M. Yang · 2021
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Sustainable modular debiasing of language models
A. Lauscher, T. Lüken, and G. Glavaš · 2021
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Towards understanding and mitigating social biases in language models
P. P. Liang, C. Wu, L.-P. Morency, and R. Salakhutdinov · 2021
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Towards a human-like open-domain chatbot
D. Adiwardana, M.-T. Luong, D. R. So, J. Hall, N. Fiedel, R. Thoppilan, Z. Yang, A. Kulshreshtha, G. Nemade, Y. Lu, et al · 2020
Cited alongside, same era.
Club: A contrastive log-ratio upper bound of mutual information
P. Cheng, W. Hao, S. Dai, J. Liu, Z. Gan, and L. Carin · 2020
Cited alongside, same era.
Towards controllable biases in language generation
E. Sheng, K.-W. Chang, P. Natarajan, and N. Peng · 2020
Cited alongside, same era.
S. Barikeri, A. Lauscher, I. Vulić, and G. Glavaš · 2021
Cited alongside, same era.
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Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp
T. Schick, S. Udupa, and H. Schütze · 2021
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Mitigating gender bias in distilled language models via counterfactual role reversal
U. Gupta, J. Dhamala, V. Kumar, A. Verma, Y. Pruksachatkun, S. Krishna, R. Gupta, K.-W. Chang, G. V. Steeg, and A. Galstyan · 2022
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What do toothbrushes do in the kitchen? how transformers think our world is structured
A. Henlein and A. Mehler · 2022
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