Exposing Bias in Online Communities through Large-Scale Language Models
Celine Wald Lukas Pfahler
Progress in natural language generation research has been shaped by the
ever-growing size of language models. While large language models pre-trained
on web data can generate human-sounding text, they also reproduce social biases
and contribute to the propagation of harmful stereotypes. This work utilises
the flaw of bias in language models to explore the biases of six different
online communities. In order to get an insight into the communities'
viewpoints, we fine-tune GPT-Neo 1.3B with six social media datasets. The bias
of the resulting models is evaluated by prompting the models with different
demographics and comparing the sentiment and toxicity values of these
generations. Together, these methods reveal that bias differs in type and
intensity for the various models. This work not only affirms how easily bias is
absorbed from training data but also presents a scalable method to identify and
compare the bias of different datasets or communities. Additionally, the
examples generated for this work demonstrate the limitations of using automated
sentiment and toxicity classifiers in bias research.