融合LLM与多BERT，提升生物医学问答系统精准度。
Optimized Biomedical Question-Answering Services with LLM and Multi-BERT Integration
- 用多层BERT与MLP结合，增强对复杂数据的处理能力。
- 在BioASQ和BioMRC数据集上实现更高效的信息整合与响应。
- 冻结部分BERT防过拟合，适合医疗专业人员快速决策使用。
我们提出一种优化的生物医学问答（QA）服务方法，通过将大语言模型（LLMs）与多BERT配置相结合，提升对海量复杂生物医学数据的处理与优先级判断能力，助力医疗从业者做出更优患者诊疗决策。通过创新整合BERT、BioBERT与多层感知机（MLP）层，系统实现更专业化、高效的问答响应，满足医疗领域日益增长的信息需求。本方法通过冻结一个BERT模型并训练另一个，有效缓解过拟合问题，显著增强系统的适应性。在BioASQ和BioMRC等大规模数据集上的实验表明，该系统具备卓越的信息合成能力。研究证明，先进语言模型能切实提升医疗服务质量，为专业人士提供可靠、快速的智能工具，推动以数据驱动的精准医疗发展。
原文摘要 · Abstract (English)
We present a refined approach to biomedical question-answering (QA) services by integrating large language models (LLMs) with Multi-BERT configurations. By enhancing the ability to process and prioritize vast amounts of complex biomedical data, this system aims to support healthcare professionals in delivering better patient outcomes and informed decision-making. Through innovative use of BERT and BioBERT models, combined with a multi-layer perceptron (MLP) layer, we enable more specialized and efficient responses to the growing demands of the healthcare sector. Our approach not only addresses the challenge of overfitting by freezing one BERT model while training another but also improves the overall adaptability of QA services. The use of extensive datasets, such as BioASQ and BioMRC, demonstrates the system's ability to synthesize critical information. This work highlights how advanced language models can make a tangible difference in healthcare, providing reliable and responsive tools for professionals to manage complex information, ultimately serving the broader goal of improved care and data-driven insights.
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