## 2 Materials and Methods
At this stage, the main goal was to verify that, in the process of training on a vast dataset, the language model can acquire sufficiently comprehensive knowledge about the locations under study to enable the model to return a correct geo-description without the need to provide any additional information (so-called zero-shot prompting). Queries like \"Generate a geo-description of the University Main Building (anonymized)\" or \"Generate a geo-description of the interior of the University Main Building (anonymized)\" were introduced. Questions about the Railway Station (anonymization) and the Mall (anonymization) were also asked. In general, the chatbot usually provides the address of the building, a general description of the building (e.g., from the architectural point of view), and its use in response to the question. According to the study authors, ChatGPT does not have access to the relevant data to provide the expected answer. The same questions were then sent to the FLAN-T5 XXL model. As with ChatGPT, it turned out that the FLAN-T5 XXL model could not generate correct geo-descriptions. As a result of this research stage, it was concluded that the current tools cannot solve this problem. Therefore, further research was designed in 3 phases:  
**Phase 1: Preparation of geo-descriptions**  
The first significant phase of the study was divided into several stages:  
1. Analysis of the available building model (2D and/or 3D map using GIS technology)  
The purpose of this phase was for the expert to develop input data for NLP--selecting places for description and extracting features of the space based on the available building model. The result of the phase was creating a list of words and phrases describing the selected places in a spatial context. 2. Preparation of model (reference) descriptions  
This step involved the preparation of geo-descriptions by an expert based on the list of words and phrases developed in the previous step. 3. Preparation of commands for NLP commissioning the task of generating geo-descriptions  
This step entailed the preparation of commands to generate geo-descriptions based on the extracted geospatial features. At this stage, phrases were combined into sentences using additional words such as \"you on\", \"you are\" and \"you at\". In addition, keywords  
Figure 2: Classification of language models by classes of NLP problems with the highlighted class Text2 Text Generation (source: [[https://huggingface.co/models](https://huggingface.co/models)]([https://huggingface.co/models](https://huggingface.co/models)), accessed on 15 August 2024). and phrases were preceded by words like \"create\", \"describe\", etc. : e.g., \"Create a location description based on the following phrases:...\", \"Describe how to get to.... based on the following phrases:...\", \"Describe my surroundings based on the following phrases:...\", \"Create warnings for people with disabilities based on the following phrases:...\", etc. Generating geo-descriptions via AI  
This step involved generating texts describing the space (geo-description) by NLP based on the commands prepared in Step 3. Evaluation  
This step consisted of evaluation of the semantic similarity of the generated geo-descriptions in Step 4 to the model ones prepared by the expert in Step 2. **Phase 2: Preparing the application for geo-description testing**  
In the second phase of the research, a mobile application (research tool) was created to verify the level of relevance and naturalness of geo-descriptions, both made by the expert and those generated through language models. In this case, relevance meant the percentage of correct indications by the surveyed people of the proper locations on the building map based on the descriptions read. **Phase 3: Conducting preliminary user studies (POC--Proof of Concept)**  
In the last phase of the research, preliminary tests were conducted on users using the application implemented in the second phase. This phase aimed to prove the feasibility of the problem posed at the beginning.