## 4 Discussion  
This research shows that using available language models is not enough to construct a command like \"Generate geo-description\" as there are as yet no such solutions that lead to the results sought after by the authors of this article. Therefore, the proposed solution aims to fill the gap in research and tool development in this area. The results obtained are promising, but at the same time it should be noted that the research conducted has several limitations:  
1. There was need to create a dedicated command scheme for each NLP technique;
2. Only partially automated analysis of the building model (sequential use of GIS analysis performed by a human) was performed;
3. The prototype of the tool (plug-in) in QGIS was made only for one type of geo-description (navigational). The most significant limitation at the first stage of the research was the need to create a dedicated command scheme for each NLP technique, with each considered separately. In addition, with the evolution of NLP techniques, the schema creation process had to be occasionally repeated. It should be noted that the analysis of the available building model was performed at this stage only by an expert, based on a 2D map of the selected building and with little support of GIS tools. Thus, this is only a simulation. An automated geometric and topological analysis of the building is necessary. First, it requires automation of the selection of points that will be subject to description (e.g., entrances/exits of the building, intersections of corridors, characteristic places of the building). Secondly, it is necessary to carry out a number of calculations, e.g., in terms of distances from the selected points to other characteristic places and to the structural objects of the building (e.g., distances to doors, windows, building edges) or area (corridor area, floor area). This type of calculation can also include neighborhood surveys, as a result of which information can be obtained about neighboring rooms and information about the building environment. Obtaining this information in an automated manner using GIS tools is relatively easy, provided that we have a properly constructed GIS vector model of the building. Currently, such models are not prevalent. However, there are many indications that the availability of such models will increase. This is evidenced, among other things, by initiatives such as the ISO's work on the standardization of BIM and GIS as manifested in the document [32]. Another example is the interoperability in linking BIM and GIS technologies by key market players in both areas, i.e., ESRI and Autodesk (the companies in 2017 announced a strategic partnership in this regard). It is also possible to develop input phrases for NLP from the analysis of the BIM models themselves saved according to the IFC standard. This issue is currently under further study by the article's authors and can be treated as a parallel process. The third limitation of the presented study is that the plug-in in QGIS for generating phrases describing the space, in its current version, allows the generation of phrases only for navigation geo-descriptions. In the next stages of the research, the tool must be expanded and generate phrases also for location, warning and cognitive geo-descriptions. It is also necessary to further expand the research with tests using other LLMs. Although the GPT-3.5 and FLAN-T5 models examined in this work produced good results,the use of newer, state-of-the-art solutions could bring further improvements and increase the versatility of the results generated. Given the very rapid development of large language models, the problems demonstrated in this work could be mitigated in the future by utilizing more advanced models. Evaluating the correctness of the geo-description is not only about assessing the space descriptions themselves.