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Found 9 papers, 5 papers with code
A Multilingual Perspective on Probing Gender Bias
Gender bias represents a form of systematic negative treatment that targets individuals based on their gender.
Grammatical Gender's Influence on Distributional Semantics: A Causal Perspective
However, when we control for the meaning of the noun, we find that grammatical gender has a near-zero effect on adjective choice, thereby calling the neo-Whorfian hypothesis into question.
Social Bias Probing: Fairness Benchmarking for Language Models
While the impact of these biases has been recognized, prior methods for bias evaluation have been limited to binary association tests on small datasets, offering a constrained view of the nature of societal biases within language models.
Measuring Intersectional Biases in Historical Documents
We find that there is a trade-off between the stability of the word embeddings and their compatibility with the historical dataset.
Optical Character Recognition
Optical Character Recognition (OCR)
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Measuring Gender Bias in West Slavic Language Models
Perhaps surprisingly, Czech, Slovak, and Polish language models produce more hurtful completions with men as subjects, which, upon inspection, we find is due to completions being related to violence, death, and sickness.
Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models
The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision.
A Latent-Variable Model for Intrinsic Probing
The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information.
Quantifying Gender Biases Towards Politicians on Reddit
Rather than overt hostile or benevolent sexism, the results of the nominal and lexical analyses suggest this interest is not as professional or respectful as that expressed about male politicians.
Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models
Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language.
