# Beyond Lexical Boundaries: LLM-Generated Text Detection for Romanian Digital Libraries
## 3 Method
### Detection Models  
We built a comprehensive pipeline dedicated to the training and assessment of a text classifier tasked with predicting the text generation model associated with a given text fragment. This involves two distinct classification techniques, each based on different  
\begin{table}
\begin{tabular}{c l l c c c} \hline \hline
**Domain** & **Method** & **Model** & **Avg TTR** & **Doc Count** & **Aggregate** \\ \hline \multirow{8}{*}{Books} & Human & Human & 0.7447 & 1401 & \multirow{8}{*}{11,208} \\  & Completion & RoGPT2 & 0.6615 & 1401 & \\  & Completion & GPT-Neo-Ro & 0.7011 & 1401 & \\  & Completion & davinci-003 & 0.6125 & 1401 & \\  & Backtranslation & davinci-003 & 0.7652 & 1401 & \\  & Paraphrasing & Flan-T5 & 0.8708 & 1401 & \\  & Backtranslation & Opus-MT & 0.7581 & 1401 & \\  & Backtranslation & mBART & 0.7379 & 1401 & \\ \hline \multirow{8}{*}{News} & Human & Human & 0.6510 & 4320 & \multirow{8}{*}{34,560} \\  & Completion & RoGPT2 & 0.6762 & 4320 & \\  & Completion & GPT-Neo-Ro & 0.6867 & 4320 & \\  & Completion & davinci-003 & 0.6508 & 4320 & \\  & Backtranslation & davinci-003 & 0.7798 & 4320 & \\  & Paraphrasing & Flan-T5 & 0.8389 & 4320 & \\  & Backtranslation & Opus-MT & 0.6589 & 4320 & \\  & Backtranslation & mBART & 0.7024 & 4320 & \\ \hline \multirow{8}{*}{Medical} & Human & Human & 0.6911 & 557 & \multirow{8}{*}{4456} \\  & Completion & RoGPT2 & 0.6795 & 557 & \\  & Completion & GPT-Neo-Ro & 0.6893 & 557 & \\  & Completion & davinci-003 & 0.6262 & 557 & \\  & Backtranslation & davinci-003 & 0.7510 & 557 & \\  & Paraphrasing & Flan-T5 & 0.8503 & 557 & \\  & Backtranslation & Opus-MT & 0.7490 & 557 & \\  & Backtranslation & mBART & 0.7618 & 557 & \\ \hline \multirow{8}{*}{Legal} & Human & Human & 0.7264 & 1000 & \multirow{8}{*}{8000} \\  & Completion & RoGPT2 & 0.6542 & 1000 & \\  & Completion & GPT-Neo-Ro & 0.6880 & 1000 & \\  & Completion & davinci-003 & 0.5828 & 1000 & \\  & Backtranslation & davinci-003 & 0.7987 & 1000 & \\  & Paraphrasing & Flan-T5 & 0.8418 & 1000 & \\  & Backtranslation & Opus-MT & 0.7231 & 1000 & \\  & Backtranslation & mBART & 0.7514 & 1000 & \\ \hline \multirow{8}{*}{RoCHI} & Human & Human & 0.6234 & 109 & \multirow{8}{*}{872} \\  & Completion & RoGPT2 & 0.6901 & 109 & \\  & Completion & GPT-Neo-Ro & 0.5460 & 109 & \\  & Completion & davinci-003 & 0.5810 & 109 & \\  & Backtranslation & davinci-003 & 0.7514 & 109 & \\  & Paraphrasing & Flan-T5 & 0.8356 & 109 & \\  & Backtranslation & Opus-MT & 0.6032 & 109 & \\  & Backtranslation & mBART & 0.7477 & 109 & \\ \hline Total & & & & & 59,096 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MGT dataset: human-written and machine-generated texts.  
architectures. The primary objective is to evaluate and compare their efficiency, particularly when incorporating additional features such as linguistic complexity into the analysis. The comprehensive approach taken in this study aims to provide insights into the effectiveness of diverse classification methodologies while considering nuanced linguistic attributes, thus contributing to a stronger understanding of AI-text detection.