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Large Language Models: A New Paradigm for Data Analysis
Tumanov O. O.

Tumanov, Oleksii O. (2025) “Large Language Models: A New Paradigm for Data Analysis.” Business Inform 9:106–113.
https://doi.org/10.32983/2222-4459-2025-9-106-113

Section: Economic statistics

Article is written in Ukrainian
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UDC 004.8:519.23

Abstract:
The emergence of large language models (LLMs) has fundamentally transformed the paradigm of data analysis, affecting fields such as social media analytics, healthcare, and software development. These models, examples of which include architectures like GPT and BERT, handle tasks related to unstructured text and contextual understanding very effectively, but their widespread adoption requires a critical assessment of their relationship to and differences from traditional statistical approaches. This article aims to provide a comparative analysis of LLMs and traditional statistical methods, examining their strengths, limitations, and areas of application. The study uses a qualitative comparison method based on the following criteria: data type (unstructured vs. structured), interpretability, purpose, data requirements, and reproducibility. The analysis shows that, despite the effectiveness of LLMs in natural language text processing, their probabilistic nature imposes significant limitations on reliability and ethics. Traditional statistical methods, in contrast, offer high interpretability and reproducibility, which are critically important for deriving deterministic conclusions. Special attention is paid to the limitations of both approaches. Specifically, LLMs are prone to «hallucinations» and inheriting biases from training data, which can lead to discriminatory outcomes. Meanwhile, traditional statistical models are not immune to selection biases. As a result, the study found that LLMs and traditional methods are not competitors but complementary tools, each best suited for addressing certain tasks. The main conclusion is that the most promising direction is the integration of these approaches into hybrid methodologies. Large language models (LLMs) can be effectively used for preprocessing and converting unstructured text into structured quantitative data, which can then be analyzed using classical statistical methods. This approach allows overcoming existing limitations and achieving greater reliability, transparency, and accuracy in scientific research.

Keywords: large language models, statistical methods, data analysis, artificial intelligence, ethical considerations, hybrid model.

Fig.: 5. Tabl.: 2. Bibl.: 12.

Tumanov Oleksii O. – PhD, Senior Lecturer, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]

List of references in article

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