## 3 Results
### Evaluation of Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model-Geo-Desc-Id** & **Model Location Geo-Description Prepared by an Expert** \\ \hline \multirow{3}{*}{1} & “You are on the floor -1, at the end of a spacious laboratory room with two columns in the middle. You are at a double roller gate with a vehicle exit ramp behind it.” \\ \hline \multirow{3}{*}{2} & “You are at the end of the southwest corridor on the first floor.” \\ \cline{2-2}  & “You are on the first floor, at the beginning of the northeastern corridor. To enter it, you must go through a fire door.” \\ \hline \multirow{3}{*}{4} & “You will find yourself on the ground floor, at the end of the \\  & north-eastern corridor, at the entrance to the conference room.” \\ \cline{2-2}  & 5 \\ \cline{2-2}  & “You are in the entrance hall on the first floor, close to the spiral staircase.” \\ \hline \multirow{3}{*}{6} & “You are in the corridor before the entrance to the toilets next to the \\  & building’s entrance hall.” \\ \cline{2-2} \end{tabular}
\end{table} 
Table 5: Model location geo-descriptions prepared by an expert (own elaboration). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model-Geo-Desc-Id**} & \multicolumn{2}{c}{**The Command for FLAN-T5 Created in Pre-Processing**} & \multirow{2}{*}{**The Result Obtained with FLAN-T5**} & \multicolumn{2}{c}{**Semantic Similarity**} \\  & & **Pre-Processing** & & **FLAN-T5—Expert** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{1} & “Create a location description based on the following phrases: you on floor -1, you at end of spacious laboratory room two columns in middle, you next to double roller date, behind gate is exit ramp for vehicles.” & \multirow{4}{*}{0.9799} \\  & & &  
To compare the NLP techniques in generating geo-descriptions, identical commands must be passed to FLAN-T5 and Chat GPT-3.5 input. Future research is planned to find universal commands using the one-shot prompting technique. **Phase 2: Methods for conducting verification studies and preparing application for testing geo-descriptions**  
The survey consisted of selecting those taking part in the study among the locations marked on the building map, that is, the map of the building that matched the geo-description provided by the mobile application. The authors hypothesized that the ease of pinpointing the right location the first time would primarily reflect the accuracy of the geo-description. The better the geo-description constructed, the easier it would be to point out the corresponding location on the map the first time. It is assumed there should be many more marked places on the map than geo-descriptions. The quality of the map and the ability of those taking part in the study to read it should be considered when analyzing the conclusions. A GeoDescTest mobile application was created specifically for this study. It can be run on devices with the Android operating system. It was made in the Android Studio environment in the Kotlin language. The map was generated using ArcGIS SDK technology. The building model was developed at the university (anonymized) as part of the \"PW Building Accessibility Maps Project\" [1]. Figure 6 shows the main screen of the application. Figure 6: Building map with a panel showing the content of the current task and with buttons for changing the active floor, changing the scale of the map, moving the map, and centering it on the most recently selected geo-description (own development). \begin{table}
\begin{tabular}{c c} \hline
**Geo-Description** & **Average Semantic** \\
**Generation Technique** & **Similarity Value (0-1)** \\ \hline google/fan-t5-xcl & 0.9399 \\ \hline \end{tabular}
\end{table} 
Table 8: The average semantic similarity value of geo-descriptions generated by a computer using the FLAN-T5 model compared to reference geo-descriptions prepared by an expert (own elaboration). At the bottom of the testing application screen, there is a panel showing the content of the current task. The user is asked to indicate the point to which the current task applies.