System Approach to the Combined Use of Large Language Models and Classical Models in Foresight Tasks

Background. Large Language Models (LLMs) and their associated agents have spread wide technology and represent a significant advancement in recent times. These state-of-the-art models expose valuable potential, but they are not devoid of restrictions, inefficiencies, and limits. This article investigates the exploration of these constraints within specific domain areas and prediction problems as examples. Objective. The article highlights features offered by GPT-based models and compares the conclusions with classical methods of textual data analysis in classification tasks using the prediction methodology as an example. The purpose of the study is to develop a system approach to the combined use of traditional machine learning approaches as a practical alternative to LLMs in foresight tasks using the example of STEEP analysis, which provides an opportunity to obtain valuable information from textual data. Methods. The study is structured into four segments, each addressing distinct parts: Data Mining, text pre-processing using LLMs, text pre-processing utilizing Natural Language Processing (NLP) methods, and comparative analysis of results. Data Mining includes data collection and data pre-processing stages for train and test observations. For the utilization of LLMs, chains of thought techniques and prompt engineering were used. Results. Throughout this study, it was acknowledged that the LLMs can be used in combination with classical machine learning methodologies for domain-specific areas in STEEP analysis under Foresight tasks. The outcome revealed a model that was developed significantly faster and with less complexity compared to LLMs such as GPT and Mistral. Increasing the number of models employed leads to more stable results. Conclusions. The main result of the proceeding is that the patterns that reveal LLMs under certain settings can also be identified by classical models. Moreover, augmenting the deployment of LLMs during the data preparation stages contributes to heightened stability in outcomes. Using classical models combined with LLMs speeds up response times during inference and reduces operating costs for running models.