ENGinius: A Bilingual LLM Optimized for Plant Construction Engineering

Abstract

Recent advances in large language models (LLMs) have drawn attention for their potential to automate and optimize processes across various sectors. However, the adoption of LLMs in the plant construction industry remains limited, mainly due to its highly specialized nature and the lack of resources for domain-specific training and evaluation. In this work, we propose ENGinius, the first LLM designed for plant construction engineering. We present procedures for data construction and model training, along with the first benchmarks tailored to this underrepresented domain. We show that ENGinius delivers optimized responses to plant engineers by leveraging enriched domain knowledge. We also demonstrate its practical impact and use cases, such as technical document processing and multilingual communication.