Specializing Small Language Models into Business and Industry Idea Reviewer Experts with Supervised Fine-Tuning - Ladislau, Gabriel Braga; Moraes, Guilherme Ratti; Zanetti, Guilherme Goes; Jacobsen, Abner Grahan; Badue, Claudine; Souza, Alberto Ferreira De; Oliveira-Santos, Thiago (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2026: Anais do XXII Simpósio Brasileiro de Sistemas de InformaçãoDO - 10.5753/sbsi.2026.248583, 2026)
Research Context: The application of Natural Language Models in industrial and business environments is rapidly expanding. While powerful, these models often require specialization to match the performance of human experts. Practical Problem: Large Language Models (LLMs) face two major barriers for enterprise adoption: 1) the lack of specific, private knowledge required for nuanced tasks, such as classifying internal company innovations, and 2) the operational costs are prohibitively high for long-term, large-scale use. Proposed Solution: We propose a cost-effective alternative by fine-tuning Small Language Models (SLMs) and encoder models (BERTs) in business ideas classification, transforming them into expert systems tailored to a company’s unique context. Related IS Theory: This research is grounded in Task-Technology Fit (TTF) theory, examining the alignment between the task’s characteristics (classifying specialized ideas) and the technology’s attributes (general-purpose LLMs vs. fine-tuned SLMs and BERTs) to determine the optimal fit. Research Method: The research involves developing and evaluating a training method for SLMs and BERTs, with real-world data augmented by an artificial dataset. Additionally, the artificial dataset creation pipeline is showcased by the research. The performance of the resulting SLMs and BERTs are then compared against that of larger, general-purpose LLMs. Results: The findings indicate that the fine-tuned SLMs and BERTs achieve superior performance on the specialized classification task compared to larger, non-fine-tuned LLMs, while significantly reducing operational costs. The results also highlight that augmenting scarce real-world data with diverse artificial data can lead to a more robust, generalizable and rich model. Contributions: This work contributes to a practical and economically viable method for specialized AI agents creation and augmentation of scarce real-world data through synthetically made datasets. Its impact lies in enabling businesses to deploy tailored, high-performing AI solutions for specific and knowledge-based tasks without the high costs of large-scale, general-purpose models.