###### Abstract
\begin{table}
\begin{tabular}{c c c} \hline
**Type of Geo-Description** & **Explanation** \\ \hline \multirow{3}{*}{Locational} & It provides the user of the LBS application with information about the current location. It allows the user to orient themselves in space and continue their independent journey to the designated destination. \\ \hline \multirow{3}{*}{Navigational} & It describes the transition between successive intermediate waypoints. \\ \hline \multirow{3}{*}{Warning} & It describes dangerous or difficult places to go. It is mainly intended for blind people, who cannot use their sense of sight to notice obstacles or dangers such as stairs, thresholds or steps. \\ \hline \multirow{3}{*}{Cognitive} & It describes selected objects (e.g., points of interest), providing a variety of information. It allows gaining knowledge of them in different contexts, primary (name, opening hours) and thematic, such as sightseeing. \\ \hline \end{tabular}
\end{table} 
Table 1: Types of geo-descriptions based on [1]. \begin{table}
\begin{tabular}{c c c c} \hline
**Navigation** & **Guidance** & **Creating Situational** & **Vulnerability to Positioning** \\
**Guidance** & **Accuracy** & **Awareness** & **System Errors** \\ \hline Turn-by-turn & High & Low & High \\ Landmark-based & Medium & Medium & Medium \\ Geo-descriptions-based & Medium & High & Low \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of different types of navigation guidance in the context of indoor navigation (own elaboration). The research presented in this article mainly concerns the third stage. To achieve this goal, it was decided to use natural language processing (NLP) methods, and, in particular, so-called large language models (LLMs). Natural language processing is a branch of artificial intelligence involving natural language translation into numbers that a computer can use to learn the world. A natural language processing system is often called a pipeline, as it usually involves several processing stages in which unprocessed text comes in from one end and processed results come out from the other [2]. In natural language processing, large language models play an essential role. Natural language processing can be divided into two main subfields: natural language understanding (NLU) and natural language generation (NLG) [3]. NLU focuses on interpreting the meaning of natural language. Through syntactic and semantic analysis, NLU helps to better understand the context of a text. NLG makes it possible to generate texts that people can understand, which is applicable to creating answers to questions, editing articles or creating dialogues. Significant developments in NLP came with the introduction of probabilistic models and machine learning techniques, particularly statistical approaches. Methods such as n-gram [4] models and hidden Markov models [5] have enabled computers to handle language patterns. In addition, the advent of large-scale linguistic resources, such as word embeddings and annotated corpora, has further accelerated the development of NLP. In recent years, deep learning has revolutionized NLP, using neural networks to learn complex linguistic representations [6]. Large language models using deep learning techniques now play a key role in the development of natural language understanding. Before the transformer architecture, the RNN (recurrent neural network) and its subtype LSTM (long short-term memory) were and still are widely used in NLP up to this day. RNNs have a recursive structure in which information is transferred from one time step to another. The article [7] presents a smart gloves for sign language interpretation using an RNN. The gloves have built-in sensors that convert hand gestures into text and speech. They used an RNN to process the signals. A paper [8] used an RNN for sentiment classification and text summarization. RNNs have an internal memory state that allows them to store information about previous sequence elements. This is useful for both mood classification and text summarization. LSTM is a subtype of RNNs.