Fetching the paper…
Reading the bibliography…
As language models are increasingly included in human-facing machine learning tools, bias against demographic subgroups has gained attention.
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
Barikeri, S.; Lauscher, A.; Vulić, I.; and Glavaš, G. 2021 · 1955
Earlier work this paper cites.
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
Nangia, N.; Vania, C.; Bhalerao, R.; and Bowman, S. R. 2020 · 1967
Earlier work this paper cites.
Efficient Estimation of Word Representations in Vector Space
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013 · 2013
Earlier work this paper cites.
Teaching Machines to Read and Comprehend
Hermann, K. M.; Kociský, T.; Grefenstette, E.; Espeholt, L.; Kay, W.; Suleyman, M.; and Blunsom, P. 2015 · 2015
Earlier work this paper cites.
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T.; Chang, K.-W.; Zou, J.; Saligrama, V.; and Kalai, A. 2016 · 2016
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A.; Bryson, J. J.; and Narayanan, A. 2017 · 2017
Earlier work this paper cites.
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
Velupillai, S.; Suominen, H.; Liakata, M.; Roberts, A.; Shah, A. D.; Morley, K.; Osborn, D.; Hayes, J.; Stewart, R.; Downs, J.; Chapman, W.; and Dutta, R. 2018 · 2018
Earlier work this paper cites.
Learning Gender-Neutral Word Embeddings
Zhao, J.; Zhou, Y.; Li, Z.; Wang, W.; and Chang, K.-W. 2018 · 2018
Earlier work this paper cites.
Identifying and Reducing Gender Bias in Word-Level Language Models
Bordia, S.; and Bowman, S. R. 2019 · 2019
Earlier work this paper cites.
Law and Word Order: NLP in Legal Tech
Dale, R. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
Measuring Bias in Contextualized Word Representations
Kurita, K.; Vyas, N.; Pareek, A.; Black, A. W.; and Tsvetkov, Y. 2019 · 2019
Cited alongside, same era.
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings
Manzini, T.; Yao Chong, L.; Black, A. W.; and Tsvetkov, Y. 2019 · 2019
Cited alongside, same era.
On Measuring Social Biases in Sentence Encoders
May, C.; Wang, A.; Bordia, S.; Bowman, S. R.; and Rudinger, R. 2019 · 2019
Cited alongside, same era.
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function
Qian, Y.; Muaz, U.; Zhang, B.; and Hyun, J. W. 2019 · 2019
Towards Debiasing Sentence Representations
Liang, P. P.; Li, I. M.; Zheng, E.; Lim, Y. C.; Salakhutdinov, R.; and Morency, L.-P. 2020 · 2020
Later among the works it cites.
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
Zhang, Y.; Sun, S.; Galley, M.; Chen, Y.-C.; Brockett, C.; Gao, X.; Gao, J.; Liu, J.; and Dolan, B. 2020 · 2020
Later among the works it cites.
Towards Understanding and Mitigating Social Biases in Language Models
Liang, P. P.; Wu, C.; Morency, L.-P.; and Salakhutdinov, R. 2021 · 2021
Later among the works it cites.
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Meade, N.; Poole-Dayan, E.; and Reddy, S. 2021 · 2021
Later among the works it cites.
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Wang, A.; Hula, J.; Xia, P.; Pappagari, R.; McCoy, R. T.; Patel, R.; Kim, N.; Tenney, I.; Huang, Y.; Yu, K.; Jin, S.; Chen, B.; Van Durme, B.; Grave, E.; Pavlick, E.; and Bowman, S. R. 2019 · 2019
Cited alongside, same era.
Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L.; Barocas, S.; Daumé III, H.; and Wallach, H. 2020 · 2020
Cited alongside, same era.
Nadeem, M.; Bethke, A.; and Reddy, S. 2021 · 2021
Later among the works it cites.
Can Pretrained Language Models Generate Persuasive, Faithful, and Informative Ad Text for Product Descriptions?
Koto, F.; Lau, J. H.; and Baldwin, T. 2022 · 2022
Later among the works it cites.
OPT: Open Pre-trained Transformer Language Models
Zhang, S.; Roller, S.; Goyal, N.; Artetxe, M.; Chen, M.; Chen, S.; Dewan, C.; Diab, M.; Li, X.; Lin, X. V.; Mihaylov, T.; Ott, M.; Shleifer, S.; Shuster, K.; Simig, D.; Koura, P. S.; Sridhar, A.; Wang, T.; and Zettlemoyer, L. 2022 · 2022
Later among the works it cites.