Domain-Specific Fine-Tuning of LLMs for Elevator Emergency Response Systems
Domain-Specific Fine-Tuning of LLMs for Elevator Emergency Response Systems
Official scientific asset for International Journal of Applied Methods in Electronics and Computers
Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their application in safety-critical domains such as elevator emergency response requires domain-specific knowledge and reliable performance, particularly when deployed as offline edge AI systems with constrained computational resources. This study presents fine-tuning of the Gemma-2-9B-Instruct model using Quantized Low-Rank Adaptation (QLoRA) for Turkish elevator emergency scenarios, targeting deployment on embedded touchscreen control panels inside elevator cabins. Unlike cloud-based systems leveraging internet connectivity for retrieval, our edge deployment operates entirely offline, making fine-tuning essential for encoding domain knowledge directly into model parameters. We developed a specialized Turkish dataset containing 1,155 question-answer pairs covering diverse emergency situations. Our evaluation demonstrates significant improvements: ROUGE-1 scores increased from 0.259 to 0.317 (22.49%), BLEU improved by 218.11%, and hallucination rates reduced from 54% to 22% while achieving 49% faster inference. The resulting 4.8GB quantized model runs entirely on embedded hardware without network dependencies, providing immediate, reliable emergency guidance. These results validate parameter-efficient fine-tuning for safety-critical edge AI applications.
Author Affiliations
- Busra Onal— Butkon Elevator Industry Trade Inc., Research and Development Center, Konya, Türkiyefingerprinthttps://orcid.org/0009-0003-0438-3440
- Muhammet Fatih Aslan— Department of Artificial Intelligence and Machine Learning, Faculty of Computer and Information Sciences, Konya Technical University, Konya 42000, Türkiyefingerprinthttps://orcid.org/0000-0001-7549-0137
- Akif Durdu— Department of Electrical-Electronic Engineering, Konya Technical Universityfingerprinthttps://orcid.org/0000-0002-5611-2322
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