The rapid development of machine learning (ML) technologies, particularly large language models (LLMs), has led to major advancements in natural language processing (NLP, Abbasi et al. 2023). While much of this advancement happened under the umbrella of the common task framework which espouses transparency and openness (Abbasi et al. 2023), in recent years, closed LLMs such as GPT-3 and GPT-4 have set new performance standards in tasks ranging from text generation to question answering, demonstrating unprecedented capabilities in zero-shot and few-shot learning scenarios (Brown et al. 2020, OpenAI 2023). Given the strong performance of closed LLMs such as GPT-4, many studies within the LLM-as-a-judge paradigm rely on their scores as ground truth benchmarks for evaluating both open and closed LLMs (Chiang and Lee 2023), further entrenching the dominance of SOTA closed LLMs (Vergho et al. 2024). Along with closed LLMs, there are also LLMs where the pre-trained models (i.e., training weights) and inference code are publicly available ("open LLMs") such as Llama (Touvron et al. 2023, Dubey et al. 2024) as well as LLMs where the full training data and training code are also available ("open-source LLMs") such as OLMo (Groeneveld et al. 2024). Open and open-source LLMs provide varying levels of transparency for developers and researchers (Liu et al. 2023). Access to model weights, training data, and inference code enables several benefits for the user-developer-researcher community, including lower costs per input/output token through third-party API services, support for local/offline pre-training and fine-tuning, and deeper analysis of model biases and debiasing strategies. However, the dominance of closed LLMs raises a number of concerns, including accessibility and fairness (Strubell et al. 2020, Bender 2021, Irugalbandara et al. 2024).

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.