  The growing capability and availability of generative language models has
enabled a wide range of new downstream tasks. Academic research has identified,
quantified and mitigated biases present in language models but is rarely
tailored to downstream tasks where wider impact on individuals and society can
be felt. In this work, we leverage one popular generative language model,
GPT-3, with the goal of writing unbiased and realistic job advertisements. We
first assess the bias and realism of zero-shot generated advertisements and
compare them to real-world advertisements. We then evaluate prompt-engineering
and fine-tuning as debiasing methods. We find that prompt-engineering with
diversity-encouraging prompts gives no significant improvement to bias, nor
realism. Conversely, fine-tuning, especially on unbiased real advertisements,
can improve realism and reduce bias.
