Comparing LLMs in business rule-following

• Nikson Bernardes Fernandes Ferreira INDT
• William Freitas INDT
• Hallyson Melo INDT
• Andre Carvalho UFAM
• Thiago Borges INDT
• Rodrigo Marques INDT

DOI: https://doi.org/10.5753/cibse.2025.35327

Abstract

Large Language Models (LLMs) have shown great capabilities in language understanding and instruction following. However, to the author’s knowledge, no prior work has evaluated their performance in real industry internal rule-following scenarios compared to humans. The present R&D project aims to analyze the applicability of LLMs in improving efficiency in task analysis and scheduling through automatic team assignment, following a set of internal business rules. The study was funded by SUFRAMA and is a collaboration between INDT and Motorola Mobility. The experiment results show that lightweight open LLMs, on average, have worse accuracy than mean worker (57.5% x 86.25%) with a higher divergence rate (90% x 45%).

Keywords: LLM, LLMs, rule-following, Business-rules, open-source models

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