Knowledge Accuracy and Response Characteristics of an On-Device Large Language Model in Building Environmental Engineering: a Preliminary Case Study of EXAONE 4.0 1.2B Using ChatGPT-4o as a Cloud Reference

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

This preliminary case study examines EXAONE 4.0 1.2B, an on-device large language model (LLM), in building environmental engineering, using ChatGPT-4o as a high-capability cloud reference rather than a size-matched competitor. Ten Korean-language items were evaluated: four multiple-choice, three single-step calculations, and three short-answer questions covering indoor environmental quality, energy, and post-occupancy evaluation. The same simple prompt condition was applied to both models, with no model-specific prompt optimization; therefore, the results represent one standardized condition rather than each model’s maximum performance. Both models answered all seven objective items correctly, although EXAONE showed terminology confusion in its reasoning on a thermal-environment item. Five doctoral-level experts rated the short-answer responses for accuracy, completeness, and logical consistency. Mean scores were 4.18 for ChatGPT-4o and 2.98 for EXAONE. Fleiss’ kappa was 0.020 and 0.044, while free-marginal kappa was 0.333 and 0.222, respectively; the latter values indicate fair, not moderate, agreement, so absolute expert scores require cautious interpretation. Qualitatively, ChatGPT-4o more consistently organized responses around concepts and scope, whereas EXAONE tended to provide specific technical, operational, and Korean-regulatory content but occasionally omitted canonical elements or added unsupported detail. Because the item pool is small and only one on-device model was tested, the findings are exploratory and cannot be generalized to building environmental engineering as a whole or to on-device LLMs as a class. The results support further investigation of on-device LLMs as supervised offline assistants where connectivity or external data transmission is constrained, but not as unsupervised tools for engineering calculation or compliance decisions.

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