###### Abstract
Its architecture provides a possibility to better model temporal dependencies in longer periods of time. This solves the problem of the fading gradient that occurs in standard RNNs. An example of the use of LSTM in NLP is the automatic generation of grammatically and semantically correct essays [9]. A paper [10] proposes an algorithm using BERT (Bilateral Encoder Representations from Transformers) to achieve contextual embedding and LSTM for sequential information processing to facilitate efficient detection of sensitive information in text documents. A large language model is an artificial intelligence algorithm that uses deep learning techniques and large datasets to understand, summarize, generate and predict new content. LLMs are models with many parameters (from tens to hundreds of billions) and extensive, diverse learning sets [11]. Another feature common to LLMs is that their architecture is based on the self-attention mechanism underlying transformer-type layers [12]. These models require vast computational resources, making them expensive to train, but their high utility has led to rapid development in recent years. With the development of large language models, it has been noted that they show the ability to solve tasks for which they were not taught initially based on queries (known as zero-shot prompting) and pointing to examples (known as few-shot prompting) without fine-tuning [13]. The fine-tuning process is understood here as updating parameter values during the model learning process. Figure 1 shows in a graph the increase in the quality of language models in zero-, one-, and few-shot prompting tasks with the rise in the number of parameters of these models and the number of examples. This phenomenon is one of the reasons for the very high popularity of LLM-based tools such as OpenAI's ChatGPT. It has also led to a new way of using and working with language models, i.e., prompt engineering. It is a technique for efficiently producing queries to language models for various specialized tasks without fine-tuning [14]. This technique has many applications in health care [15] or chemistry [16]. Moreover, it is also used in the context of spatial data analysis. An example is the work where zero-shot prompting was used to automatically suggest OpenStreetMaps tags ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), accessed on 10 July 2024) based on data automatically extracted from images [17]. Another example of the application of this technique is robot navigation. A paper [18] proposes a system capable of translating a user's natural language commands into a series of commands that can be interpreted by a robot and converted into navigation tasks. The paper [19] proposed the concept of a system for generating descriptions tailored to a user's profile from various random topics using natural language models. [PERSON] at al. [20] presented the concept of the WorldScribe system designed for blind users to automatically generate visual descriptions of the environment. The system adapts descriptions to visual context (shorter descriptions for dynamic scenes, longer descriptions for stable scenes) and audio context (e.g., increasing volume in noisy environments). WorldScribe uses image, language and audio recognition models to provide real-time descriptions tailored to the user. The article [21] presents the development of a mobile application using augmented reality (AR) techniques and rMQR (Rectangular Micro QR Code) codes to reproduce acoustic location descriptions. [PERSON] et al. [22] have developed a sonification interface that allows visually impaired people to explore complex indoor spaces using a smartphone. The user is informed of the geometry of the objects being explored by sliding a finger across the device's touch interface. The article [23] presents the development of a mobile application for visually impaired people to recognize space by automatically detecting objects.