Knowledge Bases (KBs) play a key role in various applications. As two representative KB-related tasks, knowledge base completion (KBC) and knowledge base question answering (KBQA) are closely related and inherently complementary with each other. Thus, it will be beneficial to solve the task of joint KBC and KBQA to make them reinforce each other. However, existing studies usually rely on the small language model (SLM) to enhance them jointly, and the large language model (LLM)'s strong reasoning ability is ignored. In this paper, by combining the strengths of the LLM with the SLM, we propose a novel framework JCQL, which can make these two tasks enhance each other in an iterative manner. To make KBC enhance KBQA, we augment the LLM agent-based KBQA model's reasoning paths by incorporating an SLM-trained KBC model as an action of the agent, alleviating the LLM's hallucination and high computational costs issue in KBQA. To make KBQA enhance KBC, we incrementally fine-tune the KBC model by leveraging KBQA's reasoning paths as its supplementary training data, improving the ability of the SLM in KBC. Extensive experiments over two public benchmark data sets demonstrate that JCQL surpasses all baselines for both KBC and KBQA tasks.

翻译：知识库在各个应用中发挥着关键作用。作为两项典型的知识库相关任务，知识库补全与知识库问答密切相关，且具有天然互补性。因此，联合求解KBC与KBQA任务以实现相互增强具有重要价值。然而现有研究通常依赖小语言模型进行联合增强，忽视了大语言模型强大的推理能力。本文通过结合LLM与SLM的优势，提出一种新型框架JCQL，该框架能以迭代方式实现两项任务的相互增强。为使KBC增强KBQA，我们将基于SLM训练的KBC模型作为智能体的一项动作，融入基于LLM智能体的KBQA模型推理路径中，从而缓解LLM在KBQA中产生的幻觉和高计算成本问题。为使KBQA增强KBC，我们利用KBQA的推理路径作为补充训练数据对KBC模型进行增量微调，提升SLM在KBC任务中的能力。在两个公开基准数据集上的大量实验表明，JCQL在KBC与KBQA两项任务上均超越了所有基线方法。