Large language models have significantly advanced the area of natural language processing, but
they inherit biases present in the training data. The learned biases can lead to discriminatory
decisions in employment, justice, and other applications. This work explores gender bias in the
Slovene masked large language model SloBERTa. We present and apply six methods for bias
analysis and adapt the English debiasing method Kaneko and Bollegala (2021) for the Slovenian
SloBERTa model. The model's performance before and after debiasing is evaluated using tasks
from the SuperGLUE benchmark and sentiment classification task with the Sentinews dataset. The
Sentence Association Test results confirm a male bias in relation to professions in the original
SloBERTa model, while the other five bias detection methods do not identify such bias. After
debiasing, none of the methods detect bias. However, the debiased models show a decline in
performance on selected tasks from the SuperGLUE benchmark and the sentiment classification
task.
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