  The emergence of large language models (LLMs), such as Generative Pre-trained
Transformer 4 (GPT-4) used by ChatGPT, has profoundly impacted the academic and
broader community. While these models offer numerous advantages in terms of
revolutionizing work and study methods, they have also garnered significant
attention due to their potential negative consequences. One example is
generating academic reports or papers with little to no human contribution.
Consequently, researchers have focused on developing detectors to address the
misuse of LLMs. However, most existing methods prioritize achieving higher
accuracy on restricted datasets, neglecting the crucial aspect of
generalizability. This limitation hinders their practical application in
real-life scenarios where reliability is paramount. In this paper, we present a
comprehensive analysis of the impact of prompts on the text generated by LLMs
and highlight the potential lack of robustness in one of the current
state-of-the-art GPT detectors. To mitigate these issues concerning the misuse
of LLMs in academic writing, we propose a reference-based Siamese detector
named Synthetic-Siamese which takes a pair of texts, one as the inquiry and the
other as the reference. Our method effectively addresses the lack of robustness
of previous detectors (OpenAI detector and DetectGPT) and significantly
improves the baseline performances in realistic academic writing scenarios by
approximately 67% to 95%.
