Traditional knowledge graph (KG) completion models learn embeddings to predict missing facts. Recent works attempt to complete KGs in a text-generation manner with large language models (LLMs). However, they need to ground the output of LLMs to KG entities, which inevitably brings errors. In this paper, we present a finetuning framework, DIFT, aiming to unleash the KG completion ability of LLMs and avoid grounding errors. Given an incomplete fact, DIFT employs a lightweight model to obtain candidate entities and finetunes an LLM with discrimination instructions to select the correct one from the given candidates. To improve performance while reducing instruction data, DIFT uses a truncated sampling method to select useful facts for finetuning and injects KG embeddings into the LLM. Extensive experiments on benchmark datasets demonstrate the effectiveness of our proposed framework.

本研究解决了传统知识图完成模型需要依赖嵌入预测缺失事实的问题，并提出了一种新的微调框架DIFT，以避免在使用大型语言模型时的结合错误。DIFT通过轻量模型获取候选实体，并利用区分指令对大型语言模型进行筛选，实验结果表明，该方法在基准数据集上展现了显著的效能提升。

通过区分指令微调生成大型语言模型