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We present BYOKG, a universal question-answering (QA) system that can operateon any knowledge graph (KG), requires no human-annotated training data, and canbe ready to use within a day -- attributes that are out-of-scope for currentKGQA systems. BYOKG draws inspiration from the remarkable ability of humans tocomprehend information present in an unseen KG through exploration -- startingat random nodes, inspecting the labels of adjacent nodes and edges, andcombining them with their prior world knowledge. In BYOKG, explorationleverages an LLM-backed symbolic agent that generates a diverse set ofquery-program exemplars, which are then used to ground a retrieval-augmentedreasoning procedure to predict programs for arbitrary questions. BYOKG iseffective over both small- and large-scale graphs, showing dramatic gains in QAaccuracy 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 BYOKGoutperforms a supervised in-context learning method, demonstrating theeffectiveness of exploration. Lastly, we find that performance of BYOKGreliably improves with continued exploration as well as improvements in thebase LLM, notably outperforming a state-of-the-art fine-tuned model by 7.08 F1on a sub-sampled zero-shot split of GrailQA.

Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA