## 5 Conclusions  
In this study, we applied an existing authorship verification model by [PERSON] et al. [22], originally trained on human-written texts, to analyze and compare the language style of different large language models. The AV model gives us a score that can be used as a writing style similarity score. The features that were important to making each prediction, which can be captured via SHAP values, allowed us to see which stylometric features were contributing to the similarity score. One of our key findings revealed that GPT3 and ChatGPT exhibit a highly consistent and unique style. On the other hand, it was interesting to see that GPT2, with an older architecture, smaller parameters, and a smaller training corpus, had a similar style to the human prompts than newer models like GPT3 and ChatGPT. One hypothesis for this behavior is that the larger models have a more defined style influenced by their more advanced architecture and would stay closer to its unique style rather than the source texts style. Another hypothesis is due to the fact that GPT2 relies on a simpler next-word prediction approach and that the output is more restrictive to the immediate context provided in the prompt, allowing GPT2 to retain stylistic elements from the source text. On the other hand, ChatGPT is optimized to be a conversational agent that is tuned to generate coherent, user-friendly, dialogue-style responses, which could have instilled a more uniform and distinct style. We also saw that LLaMA texts exhibited stylistic similarities with the GPT3-family models. One possible reason for this could be due to overlapping training datasets and the similarities in the architectural approaches between LLaMA and the GPT3 variants. One advantage of using the [PERSON] et al. [22] AV model is that we were able to perform feature analyses to understand which features were contributing to model predictions. These analyses allow us to answer the three research questions outlined: What features distinguish the language style of AI and human texts, what features are different across different AI models, and how do the features change over different AI model versions? Answering these questions helped us uncover some interesting themes. The GPT3/ChatGPT models were the most distinguishable from human texts. One of the most prominent features in ChatGPT, GPT3, and LLaMA texts was the phrase \"_t help but_\" referring to phrases like \"_can't help but wonder_\" and \"_can't help but think_\". Upon a manual inspection of the generated texts, we saw that all three models had this phrase occurring more often compared to GPT2. Phrases with the form \"[past tense verb], _his_\" also distinguished these three models from human texts. While GPT2 texts were less distinguishable from human texts, the features driving these predictions were mostly punctuation-related features. Unlike the other models, we did not observe specific phrases driving these predictions. Another interesting observation was that models of the same family (GPT3 version models: ChatGPT and GPT3) were stylistically very similar. Based on the current experiments we conducted, the pattern discovered was that as the models became more complex, they became more distinguishable from human texts and seemed to converge on a more unique style for each model/model family. While these findings provide insights into the language style of different LLMs, several limitations in our study must be addressed to refine and expand upon these observations:  
* **GPT2 scores:** Our current results show that GPT2 had a higher stylistic similarity to the human prompts. We discussed some of our hypotheses for the observation above. Future work can validate these by more closely analyzing the GPT2-generated texts that were predicted to be more similar to human texts. Furthermore, we can improve the underlying authorship verification model, informed by the results of this feature analysis. * **Fine-tuning:** Models like GPT2 and LLaMA allow for fine-tuning their models.