## 4 Results and Discussion
### RQ6: GenAI or Machine Translators? In this analysis, the results are categorized based on their origin, either from a machine translator or a generative AI system (GenAI). Table 18 presents the outcomes of the word embedding test, which are organized according to this classification, alongside the results from INCIBE's translations. The META model emerges as the most rigorous in terms of semantic similarity. In contrast, the Corpus (INCIBE) exhibits greater variability in its evaluations, suggesting a heightened sensitivity to variations in the input data. According to the DB (INCIBE) model, GenAI achieves the highest average quality in terms of semantic similarity; however, its variability is notably broader compared to the INCIBE translations. GenAI continues to outperform other systems in the Corpus (INCIBE) model, yet INCIBE translations demonstrate greater consistency than those produced by machine translators. In the META model, INCIBE achieves both the highest mean score and the lowest variability, while GenAI struggles with consistency despite securing a greater number of absolute victories. It can be inferred that the META model favors the INCIBE reference translation, likely due to the closer alignment of its training or reference data with those used in the INCIBE \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ChatGPT** & **Bing** & **DeepL** & **Google Translate** & **Copilot** \\ \hline Time/Translation (s) & 5.4055 & 1.4 & 7.0328 & 3.2 & 4.9948 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Extraction times for the translations from each IAG in seconds. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Model** & **Value** & **INCIBE** & **GenAI** & **Machine Translators** \\ \hline \multirow{5}{*}{DB (INCIBE)} & \(\overline{X}\) & 0.8390 & 0.8484 & 0.8440 \\  & S & 0.0346 & 0.0364 & 0.0340 \\  & Maximum & 0.9748 & 0.9748 & 0.9753 \\  & Minimum & 0.7315 & 0.6957 & 0.6991 \\  & Victories & 1058 & 3240 & 3216 \\ \hline \multirow{5}{*}{Corpus (INCIBE)} & \(\overline{X}\) & 0.7746 & 0.7846 & 0.7757 \\  & S & 0.0520 & 0.0556 & 0.0519 \\ \cline{1-1}  & Maximum & 0.9729 & 0.9735 & 0.9736 \\ \cline{1-1}  & Minimum & 0.5891 & 0.5628 & 0.5635 \\ \cline{1-1}  & Victories & 1821 & 3188 & 2505 \\ \hline \multirow{5}{*}{META} & \(\overline{X}\) & 0.9122 & 0.8952 & 0.9104 \\  & S & 0.0203 & 0.0824 & 0.0255 \\ \cline{1-1}  & Maximum & 0.9607 & 0.9625 & 0.9601 \\ \cline{1-1}  & Minimum & 0.7653 & 0.4111 & 0.4861 \\ \cline{1-1}  & Victories & 1909 & 3333 & 2272 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Word Embedding results with the 3 different models: DB is the INCIBE database, and Corpus is an INCIBE Corpus. corpus. If DB (INCIBE) serves as the reference database containing all translations, it is logical that the scores would be higher in this model as its evaluations would be more closely aligned with the original reference. Nevertheless, the META model overall produces the highest mean scores. If META employs a broader and more generalized vector space, it may be more robust and capable of identifying semantic similarities with greater precision, thus generating higher scores across the board. Conversely, if DB (INCIBE) utilizes a more restricted vector space or is biased toward a specific type of translation, it may struggle to capture variations introduced by GenAI and machine translators, potentially leading to lower scores for translations that alter word order and structure while preserving semantic meaning. Another notable observation is that GenAI exhibits the highest variability within the META model, indicating greater fluctuations in its scores. META appears to be more sensitive to substantial differences in translations generated by GenAI, while both DB (INCIBE) and Corpus (INCIBE) impose stricter penalties for structural changes, with META being more accommodating in this regard.