  The rapid development of large language models (LLMs) has significantly
improved the generation of fluent and convincing text, raising concerns about
their misuse on social media platforms. We present a methodology using Twitter
datasets to examine the generative capabilities of four LLMs: Llama 3, Mistral,
Qwen2, and GPT4o. We evaluate 7B and 8B parameter base-instruction models of
the three open-source LLMs and validate the impact of further fine-tuning and
"uncensored" versions. Our findings show that "uncensored" models with
additional in-domain fine-tuning dramatically reduce the effectiveness of
automated detection methods. This study addresses a gap by exploring smaller
open-source models and the effects of "uncensoring," providing insights into
how fine-tuning and content moderation influence machine-generated text
detection.
