## 2 Materials and Methods
### Assessment  
Evaluation is an essential stage in developing any machine or deep learning model that provides a clear understanding of model performance and highlights areas for potential improvements [32; 33; 34]. Model evaluation is performed by applying precision, accuracy, recall, and ROC/AUC metrics, comparing the model responses with the expected responses. These metrics provide a comprehensive view of the model's performance, identifying its ability to respond correctly and effectively to understand and process the variations and complexities of natural language. By analyzing these indicators, you can identify areas where the model excels and those where improvements are required, which is crucial for the continued development and optimization of the chatbot. Metrics:  
* Accuracy: This metric indicates how many of the positive classifications made by the model are positive. It is calculated using the following formula: \[\textit{Precision}=\frac{\textit{True positives}\ (\textit{TP})}{\textit{True positives}\ (\textit{TP})+\textit{False positives}(\textit{FP})}\] (1)
* Recall or sensitivity: Represents how many of the actual positive classifications were captured by the model. Its formula is as follows: \[\textit{Recall}=\frac{\textit{True positives}\ (\textit{TP})}{\textit{True positives}\ (\textit{TP})+\textit{False negatives}(\textit{FN})}\] (2)
* F1-Score: It is a metric that combines both precision and recall in a single number, providing a balance between both metrics. It is beneficial when classes are unbalanced. It is calculated as follows: \[\textit{F1 Score}=\frac{2*\textit{Accuracy}*\textit{Recall}}{\textit{ Accuracy}+\textit{Recall}}\] (3)
* ROC-AUC (Receiver Operating Characteristic--Area Under the Curve): This metric is handy for binary classification problems. The ROC curve plots the recall against the false positive rate, and the AUC measures the total area under this curve. An AUC of 1 indicates perfect classification, while an AUC of 0.5 suggests that the model has no discriminative ability [35]. \begin{table}
\begin{tabular}{c c c c} \hline
**NLP Tool** & **Processing Technique** & **Contextual Analysis** & **Application in Answers** \\ \hline BERT (Bidirectional Encoder Representations from Transformers) & Bidirectional processing of the complete sentence. & High & Deep understanding of the context of words in sentences. \\ GPT (Generative Pre-trained Transformer) & Transformer-based text generation. & Moderate & Generation of fluid and coherent responses. \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of NLP Tools: features and applications of BERT and GPT. The recall presents a value of 94.8%; this tells us that the model could correctly identify 94.8% of all the actual positive samples in the test set. A high recall, like the one we have, is essential in contexts where it is crucial to detect all real positives, minimizing false negatives. The F1-Score, with 95.1%, is a metric that combines both precision and recall into a single value, providing a balance between the two. An F1-Score close to 100% indicates a good balance between precision and recall. In this case, our model shows balanced and robust performance in both dimensions. The ROC-AUC curve presented a value of 0.989, approaching the optimal value of 1.0. This metric evaluates the model's ability to distinguish between positive and negative classes. A higher value indicates that the model has a high discriminative ability. The MSE resulted in a value of 0.027; this metric indicates the mean squared error between the model predictions and the actual values. In regression contexts, a lower value of MSE is desirable. While this value seems low, comparing it with an acceptable range specific to the domain or problem is vital to determining its relevance. It is possible to make the following observations regarding the cases in which these values were obtained. The high precision results from a well-balanced data set has proper preprocessing that removes noise or irrelevant features.