用本体对齐降低大模型认知不确定性，提升生成准确率。
Can Structured Data Reduce Epistemic Uncertainty?
- 通过本体对齐增强模型学习，加速下游任务训练
- 使LLM生成内容上下文相似度提升8.97%，事实准确率升1%
- 提出幻觉指数，证明该方法可降低4.847%的幻觉
本文提出一种利用本体对齐改进深度学习模型训练过程的框架。实验表明，基于本体微调的模型在序列分类任务中学习速度更快、性能更优。进一步拓展发现，本体对齐过程中获取的蕴含映射能提升大型语言模型的检索增强生成效果：生成内容的上下文相似度提高8.97%，事实准确性提升1%。我们据此定义了幻觉指数，结果显示该方法使大模型幻觉减少4.847%。
原文摘要 · Abstract (English)
In this work, we present a framework that utilizes ontology alignment to improve the learning process of deep learning models. With this approach we show that models fine-tuned using ontologies learn a downstream task at a higher rate with better performance on a sequential classification task compared to the native version of the model. Additionally, we extend our work to showcase how subsumption mappings retrieved during the process of ontology alignment can help enhance Retrieval-Augmented Generation in Large Language Models. The results show that the responses obtained by using subsumption mappings show an increase of 8.97% in contextual similarity and a 1% increase in factual accuracy. We also use these scores to define our Hallucination Index and show that this approach reduces hallucination in LLMs by 4.847%.
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