While GPT-3 has garnered significant attention for its capabilities in natural language generation, research on its use outside of English is still relatively limited. We focus on how GPT-3 can be fine-tuned for generating synthetic news articles in a low-resource language, namely Danish. The model’s performance is evaluated on the dimensions of human and machine detection in two separate experiments. When presented with either a real or GPT-3 generated news article, human participants achieve a 58.1% classification accuracy. Contrarily, a fine-tuned BERT classifier obtains a 92.7% accuracy on the same task. This discrepancy likely pertains to the fine-tuned GPT-3 model oversampling high-likelihood tokens in its text generation. Although this is undetectable to the human eye, it leaves a statistical discrepancy for machine classifiers to detect. We address how decisions in the experimental design favoured the machine classifiers over the human evaluators, and whether the produced synthetic articles are applicable in a real-world context.

The presence of bias is a pressing concern for both engineers and users of language technology. What is less clear is how exactly bias can be measured, so as to rank models relative to the biases they display. Using an innovative experimental method involving data augmentation, we measure the effect of intersectional biases in Danish models used for Name Entity Recognition (NER). We quantify differences in representational biases, understood as a systematic difference in error or what is called error disparity. Our analysis includes both gender and ethnicity to illustrate the effect of multiple dimensions of bias, as well as experiments which look to move beyond a narrowly binary analysis of gender. We show that all contemporary Danish NER models perform systematically worse on non-binary and minority ethnic names, while not showing significant differences for typically Danish names. Our data augmentation technique can be applied on other languages to test for biases which might be relevant for researchers applying NER models to the study of cultural heritage data.