基于协同专家系统的建筑施工大语言模型问答系统OA

To address the issues of hallucinated generation and high deployment costs encountered by large language model(LLM)-based question answering systems in construction scenarios,this paper proposes a construction-oriented question answering system based on a collaborative expert mechanism.The system integrates shared experts and routing experts in a coordinated manner,which significantly improves the accuracy of answer generation and inference efficiency while preserving the model's expressive capacity and reducing computational overhead.In addition,a domain knowledge base-injected fine-tuning strategy is introduced to guide the model to deeply learn professional semantics in the construction domain during training,thereby enhancing its understanding of engineering-related texts and ensuring that the generated responses better align with practical engineering requirements.Experimental results demonstrate that,with only approximately one-third of the model parameters activated,the proposed system achieves a generation semantic similarity of 81.1％,effectively balancing efficiency and performance and providing an efficient,reliable,and construction-specific intelligent decision-support tool for construction management.