用知识图谱增强大模型，提升零样本实体消歧准确率
Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation
- 利用知识图谱的层级结构逐步缩小候选实体范围
- 结合实体描述和图谱知识，使提示词包含更多事实信息
- 在多个数据集上优于传统方法，且适应性更强
大语言模型在自然语言处理任务中表现出色，尤其在零样本或少样本场景下无需微调即可运行。然而，它们存在幻觉、知识过时或特定领域信息缺失等问题，重新训练成本高昂。为此，本文提出使用知识图谱（KG）作为外部结构化知识源，增强大模型在零样本实体消歧（ED）中的表现。通过利用知识图谱中实体类别的层次结构，逐步缩小候选实体空间，并融合实体描述以丰富输入提示。实验表明，该方法在多个主流ED数据集上优于未增强及仅用描述增强的模型，且比专用模型更具适应性。此外，我们进行了误差分析，探讨了知识图谱语义表达能力对消歧性能的影响。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) have positioned them as a prominent solution for Natural Language Processing tasks. Notably, they can approach these problems in a zero or few-shot manner, thereby eliminating the need for training or fine-tuning task-specific models. However, LLMs face some challenges, including hallucination and the presence of outdated knowledge or missing information from specific domains in the training data. These problems cannot be easily solved by retraining the models with new data as it is a time-consuming and expensive process. To mitigate these issues, Knowledge Graphs (KGs) have been proposed as a structured external source of information to enrich LLMs. With this idea, in this work we use KGs to enhance LLMs for zero-shot Entity Disambiguation (ED). For that purpose, we leverage the hierarchical representation of the entities' classes in a KG to gradually prune the candidate space as well as the entities' descriptions to enrich the input prompt with additional factual knowledge. Our evaluation on popular ED datasets shows that the proposed method outperforms non-enhanced and description-only enhanced LLMs, and has a higher degree of adaptability than task-specific models. Furthermore, we conduct an error analysis and discuss the impact of the leveraged KG's semantic expressivity on the ED performance.
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