Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Dawkins, Hillary"'
Autor:
Guo, Rongchen, Nejadgholi, Isar, Dawkins, Hillary, Fraser, Kathleen C., Kiritchenko, Svetlana
This work provides an explanatory view of how LLMs can apply moral reasoning to both criticize and defend sexist language. We assessed eight large language models, all of which demonstrated the capability to provide explanations grounded in varying m
Externí odkaz:
http://arxiv.org/abs/2410.00175
Large language models (LLMs) have advanced to a point that even humans have difficulty discerning whether a text was generated by another human, or by a computer. However, knowing whether a text was produced by human or artificial intelligence (AI) i
Externí odkaz:
http://arxiv.org/abs/2406.15583
Publikováno v:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Mitigation of gender bias in NLP has a long history tied to debiasing static word embeddings. More recently, attention has shifted to debiasing pre-trained language models. We study to what extent the simplest projective debiasing methods, developed
Externí odkaz:
http://arxiv.org/abs/2403.18803
Autor:
Dawkins, Hillary
Publikováno v:
Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing, 103-111 (2021)
We observe an instance of gender-induced bias in a downstream application, despite the absence of explicit gender words in the test cases. We provide a test set, SoWinoBias, for the purpose of measuring such latent gender bias in coreference resoluti
Externí odkaz:
http://arxiv.org/abs/2109.14047
Autor:
Dawkins, Hillary
Publikováno v:
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 4214-4226 (2021)
Reporting and providing test sets for harmful bias in NLP applications is essential for building a robust understanding of the current problem. We present a new observation of gender bias in a downstream NLP application: marked attribute bias in natu
Externí odkaz:
http://arxiv.org/abs/2109.14039
Publikováno v:
Phys. Rev. A 102, 022220, (2020)
The characterization of errors in a quantum system is a fundamental step for two important goals. First, learning about specific sources of error is essential for optimizing experimental design and error correction methods. Second, verifying that the
Externí odkaz:
http://arxiv.org/abs/2008.09197
Autor:
Herman, Bernease, Proksch, Gundula, Berney, Rachel, Dawkins, Hillary, Kovacs, Jacob, Ma, Yahui, Rich, Jacob, Tan, Amanda
The University of Washington eScience Institute runs an annual Data Science for Social Good (DSSG) program that selects four projects each year to train students from a wide range of disciplines while helping community members execute social good pro
Externí odkaz:
http://arxiv.org/abs/1710.02447
We address previous hypotheses about possible factors influencing the gender gap in attainment in physics. Specifically, previous studies claim that male advantage may arise from multiple-choice style questions, and that scaffolding may preferentiall
Externí odkaz:
http://arxiv.org/abs/1704.07447
Autor:
Howard, Mark, Dawkins, Hillary
Publikováno v:
Eur. Phys. J. D (2016) 70: 55
Magic state distillation is a critical component in leading proposals for fault-tolerant quantum computation. Relatively little is known, however, about how to construct a magic state distillation routine or, more specifically, which stabilizer codes
Externí odkaz:
http://arxiv.org/abs/1512.04765
Publikováno v:
Phys. Rev. A 93, 023602 (2016)
Ultracold atomic Fermi gases have been a popular topic of research, with attention being paid recently to two-dimensional (2D) gases. In this work, we perform T=0 ab initio diffusion Monte Carlo calculations for a strongly interacting two-component F
Externí odkaz:
http://arxiv.org/abs/1511.05123