## 3 Results
### Evaluation of Results  
Based on the commands prepared in the previous step of the study, 18 geo-descriptions were generated. It should be noted that ChatGPT-3.5 and FLAN-T5 XXL did not have access to reference geo-descriptions. Otherwise, only modified versions of the reference geo-descriptions would have been generated instead of generating geo-descriptions based on the input phrases. The results obtained by the models selected above were compared to the expert-prepared descriptions. For this purpose, the cosine distance measure was used (Figure 4):  
\[SIM_{C}(\overrightarrow{t_{a}},\overrightarrow{t_{b}})=\frac{\overrightarrow {t_{a}}\cdot\overrightarrow{t_{b}}}{|\overrightarrow{t_{a}}|\times| \overrightarrow{t_{b}}|}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Data** & **Command (Prompt)** & **Geo-Description Generated by FLAN-T5 XXL** \\ \hline \multirow{3}{*}{Center of corridor, second floor} & Create a location description based on the following phrases: “you at center of corridor”, “you on 2 floor” & You are in the center of the corridor on the 2 nd floor. \\ \cline{1-1} \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example of using the command structure diagram (own elaboration). Figure 4: Cosine distance of two text documents (source: Similarity Measures for Text Document Clustering). In the formula above, \(\overset{\rightarrow}{t_{a}}\) and \(\overset{\rightarrow}{t_{b}}\) are m-dimensional vectors in the set of terms \(T\) = {\(t1\),..., \(tm\)}. Each dimension represents a term in the document with a non-negative weight assigned to it [31]. The cosine distance of two documents \(d1\) and \(d2\) is the normalized scalar product of the vectors \(d1\) and \(d2\) (Figure 5). It represents the cosine of the angle between the two vectors representing the documents. Documents \(d1\) and \(d2\) are identical if the value of the cosine measure is \(1\). Documents \(d1\) and \(d2\) are not similar if the value of the cosine measure is \(0\):  
This measure was chosen for two reasons:  
1. It considers the semantic similarity between two geo-descriptions, not just similarity at the level of individual words and characters; two geo-descriptions may appear to be different in vocabulary but retain identical context. 2. It is commonly used to compare similarities between text documents. Model location geo-descriptions prepared by the expert are shown below in Table 5. The effects of generating location geo-descriptions using FLAN-T5 and using Chat GPT 3.5 are summarized in Tables 6 and 7, along with an assessment of semantic similarity. It should be noted that the commands for FLAN-T5 differ from those for ChatGPT-3.5, as the models studied respond differently to the same input data. This is due to differences in the architecture of the models used, characteristics of the training datasets, hyperparameters (e.g., number of layers), and optimization techniques (e.g., clipping to help control model size). Table 8 shows the average semantic similarity value of the geo-descriptions generated using the FLAN-T5 model compared to the reference geo-descriptions prepared by the expert. The value of 0.9399 indicates very high agreement between the geo-descriptions generated by the FLAN-T5 XXL model and the geo-descriptions prepared by the expert. This means that the model is able to produce descriptions that are almost semantically identical to human descriptions. This result shows that the large language model approach can be effective in generating geo-descriptions. Figure 5: Comparing the semantic similarity of two documents (source: Similarity Measures for Text Document Clustering).