We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within a day -- attributes that are out-of-scope for current KGQA systems. BYOKG draws inspiration from the remarkable ability of humans to comprehend information present in an unseen KG through exploration -- starting at random nodes, inspecting the labels of adjacent nodes and edges, and combining them with their prior world knowledge. In BYOKG, exploration leverages an LLM-backed symbolic agent that generates a diverse set of query-program exemplars, which are then used to ground a retrieval-augmented reasoning procedure to predict programs for arbitrary questions. BYOKG is effective over both small- and large-scale graphs, showing dramatic gains in QA accuracy over a zero-shot baseline of 27.89 and 58.02 F1 on GrailQA and MetaQA, respectively. On GrailQA, we further show that our unsupervised BYOKG outperforms a supervised in-context learning method, demonstrating the effectiveness of exploration. Lastly, we find that performance of BYOKG reliably improves with continued exploration as well as improvements in the base LLM, notably outperforming a state-of-the-art fine-tuned model by 7.08 F1 on a sub-sampled zero-shot split of GrailQA.

翻译：我们提出BYOKG，一种通用问答系统，可在任意知识图谱上运行，无需人工标注训练数据，并能在一天内投入使用——这些属性超出了当前知识图谱问答系统的能力范围。BYOKG的灵感来源于人类通过探索理解陌生知识图谱信息的卓越能力：从随机节点出发，检查相邻节点与边的标签，并将其与已有世界知识结合。在BYOKG中，探索过程利用基于大语言模型的符号化智能体生成多样化的查询程序示例，这些示例随后用于支撑检索增强推理过程，从而为任意问题预测对应程序。BYOKG在小型与大型图谱上均表现有效，在GrailQA和MetaQA数据集上，相比零样本基线方法，问答准确率分别提升27.89和58.02 F1值。在GrailQA上，我们进一步证明无监督的BYOKG优于有监督的语境学习方法，彰显了探索的有效性。最后，我们发现BYOKG的性能随持续探索以及基础大语言模型的改进而稳定提升，在GrailQA零样本子集上显著超越当前最优微调模型7.08 F1值。