When Automated Assessment Meets Automated Content Generation: Examining
  Text Quality in the Era of GPTs
Marialena Bevilacqua Kezia Oketch Ruiyang Qin Will Stamey Xinyuan Zhang Yi Gan Kai Yang Ahmed Abbasi
The use of machine learning (ML) models to assess and score textual data has
become increasingly pervasive in an array of contexts including natural
language processing, information retrieval, search and recommendation, and
credibility assessment of online content. A significant disruption at the
intersection of ML and text are text-generating large-language models such as
generative pre-trained transformers (GPTs). We empirically assess the
differences in how ML-based scoring models trained on human content assess the
quality of content generated by humans versus GPTs. To do so, we propose an
analysis framework that encompasses essay scoring ML-models, human and
ML-generated essays, and a statistical model that parsimoniously considers the
impact of type of respondent, prompt genre, and the ML model used for
assessment model. A rich testbed is utilized that encompasses 18,460
human-generated and GPT-based essays. Results of our benchmark analysis reveal
that transformer pretrained language models (PLMs) more accurately score human
essay quality as compared to CNN/RNN and feature-based ML methods.
Interestingly, we find that the transformer PLMs tend to score GPT-generated
text 10-15\% higher on average, relative to human-authored documents.
Conversely, traditional deep learning and feature-based ML models score human
text considerably higher. Further analysis reveals that although the
transformer PLMs are exclusively fine-tuned on human text, they more
prominently attend to certain tokens appearing only in GPT-generated text,
possibly due to familiarity/overlap in pre-training. Our framework and results
have implications for text classification settings where automated scoring of
text is likely to be disrupted by generative AI.