基于路径选择的知识图谱增强大语言模型
Knowledge Graph-Enhanced Large Language Models via Path Selection
AI总结：
针对大语言模型的幻觉问题及现有知识图谱增强方法灵活性不足、易忽略间接语义知识的缺陷，提出三阶段框架KELP，通过潜在语义匹配实现细粒度知识提取并纳入间接语义路径，经真实数据集实验验证有效。
摘要：
大语言模型（LLM）已在各类实际应用中展现出前所未有的性能，但众所周知，它们会生成与事实不符的输出，即所谓的幻觉问题。近年来，整合从知识图谱（KG）中提取的外部知识，已成为提升LLM生成内容事实准确性的一种颇具前景的策略。然而，现有多数研究都依赖LLM自身完成KG知识提取，这种方式灵活性极差，因为LLM仅能对某条知识（例如KG中的知识路径）是否应被使用给出二元判断。此外，LLM往往只选取与输入文本存在直接语义关联的知识，而那些具有间接语义、但可能有用的知识则可能被忽略。在本研究中，我们提出了名为KELP的三阶段原则性框架以解决上述问题。具体而言，KELP通过潜在语义匹配为知识路径与输入文本生成评分，能够实现更细粒度的灵活知识提取；同时，通过在KG中选定的路径与输入文本之间训练编码机制，与输入文本存在间接语义关系的知识路径也能被纳入考量。在真实数据集上的实验验证了KELP的有效性。
英文摘要：
Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. However, they are known to generate factually inaccurate outputs, a.k.a. the hallucination problem. In recent years, incorporating external knowledge extracted from Knowledge Graphs (KGs) has become a promising strategy to improve the factual accuracy of LLM-generated outputs. Nevertheless, most existing explorations rely on LLMs themselves to perform KG knowledge extraction, which is highly inflexible as LLMs can only provide binary judgment on whether a certain knowledge (e.g., a knowledge path in KG) should be used. In addition, LLMs tend to pick only knowledge with direct semantic relationship with the input text, while potentially useful knowledge with indirect semantics can be ignored. In this work, we propose a principled framework KELP with three stages to handle the above problems. Specifically, KELP is able to achieve finer granularity of flexible knowledge extraction by generating scores for knowledge paths with input texts via latent semantic matching. Meanwhile, knowledge paths with indirect semantic relationships with the input text can also be considered via trained encoding between the selected paths in KG and the input text. Experiments on real-world datasets validate the effectiveness of KELP.
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