## 5 Conclusions
Our initial attempts to fine-tune GPT2 on the fanfiction dataset yielded low-quality outputs that were very easily detected as different from the source text with our AV model. Future research could explore advanced fine-tuning techniques, and fine-tuning on each human-author text individually would be an interesting experiment. * **Prompt design:** Our experiments used a consistent prompt template. Varying the prompts could yield richer insights into how prompt structures influence style transfer and AV scores. * **Evaluation across other domains:** Text was generated using the fanfictions that are similar to those that were used to train the AV model. Repeating the experiments across diverse textual domains (e.g., news articles, essays) would test the generalizability of our findings. * **Evaluating on newer models:** While these experiments were being conducted, several new language models such as OpenAI's GPT 4 versions [53], Google's Gemini [54], and DeepSeek [55] have become available. It is clear that language models will be constantly updated and new versions will be released. Repeating the same experiment on these newer models and evaluating their trends would be interesting research. To this end, as mentioned in Section 3.5, the source code and the current datasets have been made available. While our application of the authorship verification model is not strictly an AI detector, we can extend this work to transform the verification problem to an AI detector as follows: First, a sample of documents generated by the target language model can be collected. Then, for each document in question, the authorship verification model can be applied across this document and all the documents in our pool of known AI-generated documents. If the average score shows a high similarity, we can conclude that the document in question is an AI-generated one. Experiments along this avenue, controlling for various aspects such as different domains and different document lengths, would result in interesting future studies. While purpose-built AI detectors will perform better at detecting AI-generated texts, we believe this work was able to make several important contributions. We were able to show that we can use stylometry to provide us with more interpretable results about the language style of generative AI models. Having a more interpretable model, i.e., being able to show that a given document has a similar writing style to a language model, is especially important in high-stakes scenarios such as with student assignments. Furthermore, the interpretability of our model helped us to identify interesting patterns across different language models, such as how newer language models became easily distinguishable because they had a more distinct language style. Since the underlying AV model is only trained on human-written documents, and since this approach is agnostic to the text generation process, this approach can be extended to new language models and fine-tuned language models relatively easily. As described above, a detector can be built simply with a pool of documents from the target language model. We believe that as newer language models are released at a fast pace, this work can be used as a blueprint to analyze the language style of these new models. Conceptualization, J.W., O.S. and R.G. ; Methodology, J.W., O.S. and R.G. ; Software, J.W., O.S. and G.S. ; Investigation, J.W., O.S. and G.S. ; Writing--original draft, J.W., O.S., G.S. and J.J.; Writing--review & editing, R.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the U.S. National Science Foundation grant number 2016061 and the New York University. Not applicable.