LISGPT: Research on the Construction of a Library and Information Science Academic LLM Based on the Boundary Knowledge Enhance Framework

Academic large language models have demonstrated transformative potential in natural language processing tasks. However, they still face significant challenges in adequately understanding highly specialized and complex domain‐specific knowledge. To address this issue, this study introduces the Boundary Knowledge Enhance (BKE) framework, which constructs a large‐scale, high‐quality professional question‐answering dataset ( n = 276,083) in the Library and Information Science (LIS) domain, specifically designed to capture the complexity of social science knowledge. Furthermore, by employing the proposed Direct Boundary Knowledge Optimization (DBKO) training method, the model's ability to comprehend and apply specialized domain knowledge is significantly enhanced. Experimental results show that LISGPT achieves superior performance compared to state‐of‐the‐art commercial models. In the literature keyword prediction task, it outperforms all baseline models with an F1 Score of 0.3973, ranking first. In the professional translation task, it reaches 99.1% of the performance level of DeepSeek‐V3‐671b, achieving an average score of 0.5971 and ranking third. Ablation studies confirm that the overall performance improvement of LISGPT after DBKO training is 2.32%. This study open‐sources the large LIS training datasets and three versions of a specialized LIS academic model, offering a practical paradigm for developing open‐source, efficient models in other humanities and social sciences domains.