TextNeX: Text Network of eXperts for Robust Text Classification—Case Study on Machine-Generated-Text Detection

Emmanuel Pintelas (), Athanasios Koursaris, Ioannis E. Livieris and Vasilis Tampakas
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Emmanuel Pintelas: Department of Mathematics, University of Patras, GR 265-00 Patras, Greece
Athanasios Koursaris: Department of Mechanical Engineering and Aeronautics, University of Patras, GR 265-00 Patras, Greece
Ioannis E. Livieris: Department of Statistics & Insurance Science, University of Piraeus, GR 185-32 Piraeus, Greece
Vasilis Tampakas: Department of Electrical and Computer Engineering, University of Peloponnese, GR 263-34 Patras, Greece

Mathematics, 2025, vol. 13, issue 10, 1-15

Abstract: Efficient and accurate text classification is essential for a wide range of natural language processing applications, including sentiment analysis, spam detection and machine-generated text identification. While recent advancements in transformer-based large language models have achieved remarkable performance, they often come with significant computational costs, limiting their applicability in resource-constrained environments. In this work, we propose TextNeX, a new ensemble model that leverages lightweight language models to achieve state-of-the-art performance while maintaining computational efficiency. The development process of TextNeX model follows a three-phase procedure: (i) Expansion : generation of a pool of diverse lightweight models via randomized model setups and variations of training data; (ii) Selection : application of a clustering-based heterogeneity-driven selection to retain the most complementary models and (iii) Ensemble optimization : optimization of the selected models’ contributions using sequential quadratic programming. Experimental evaluations on three challenging text classification datasets demonstrate that TextNeX outperforms existing state-of-the-art ensemble models in accuracy, robustness and computational effectiveness, offering a practical alternative to large-scale models for real-world applications.

Keywords: natural language processing; text classification; lightweight transformer-based models; machine-generated text detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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