## 2 Materials and Methods  
At this stage, it was assumed that the recipient of the geo-description is a person using the application to help find any room in a building under standard circumstances (without danger, without the need to obtain detailed technical information, etc.). Before starting the research, we decided to check whether NLP tools exist to generate textual descriptions of spaces automatically. In analyzing the available NLP tools for their ability to create geo-descriptions, the focus was on those that best realized two essential criteria:  
1. Command compatibility The geo-description must be generated in a way that is relevant to the mandated task. For example, for a navigation-type description, the generated command should be generated in the form of instructions--with the inclusion of commands such as \"head along\", \"go\" and \"turn\". 2. Grammatical correctness The geo-description should be grammatically correct and contain only occasional punctuation errors. Due to its popularity and widespread use, the obvious first choice was to use the ChatGPT chatbot version 3.5. The GPT-3.5-turbo model is based on a transformer-based architecture [12]. In the case of this model, a version adapted to operate as a chatbot (and, therefore, remember previous questions and answers) was used. Hereafter, this model will be referred to as GPT-3.5, which is used for the model itself, or ChatGPT for the service under analysis. However, it is a closed-source model. Thus, there is no official information on the exact architecture of the solution and the number of parameters, and it is impossible to use this tool in automating the generation of geo-descriptions, which is the goal of the research described. However, it is an essential tool in verifying the development level of existing technologies. For the above reasons, it was decided to use at least one other solution in the experiments. For this purpose, the popular platform HuggingFace, which provides open language models designed to solve NLP problems, was used. The platform allows searching for models grouped by classes of NLP problems. Of the available tags (Figure 2), Text2 TextGeneration was selected:  
After selecting the tag of interest, a list of models sorted by trending is displayed. At the time of research, some of the most trending were vicuna-13B-v1.5-16K-GPTQ and google/flan-t5-xcl. It was finally decided that we would use the well-known and widely used google/flan-t5-xcl model, which uses the Text-to-Text Transfer Transformer (T5) architecture [26]. In further studies, other models will also be tested for control purposes. The T5 architecture is designed to perform multiple NLP tasks with a single, universal model. It is an encoder-decoder architecture based on transformers, similar to ChatGPT-3.5. The research used an XXL (11 billion parameters) version of the T5 model fine-tuned by the Flan method (fine-tuning language models) [27] using massive datasets involving multiple text analysis tasks [28]. Hereafter, this model will be referred to as Flan-T5 XXL. Of course, there are now a great number of other, more recent LLMs available, whether commercially released (such as GPT-4) or open source (like LLMaMa [29] or BERT [30]). The models selected for this study are examples of possible solutions, and since the performance of LLMs in generating geo-descriptions has not been studied before, we decided to initiate the study with verified and well-known models that can serve as a basis for further research in this area. With the tools selected, a command was formulated to generate a spatial description of the interior of the university building (well-known to this study's authors) to analyze the responses and preliminarily assess the applicability of NLP techniques to this task.