Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have driven significant progress in unified structural QA frameworks like TrustUQA. However, these frameworks face challenges when applied to small-scale LLMs since small-scale LLMs are prone to errors in generating structured queries. To improve the structured data QA ability of small-scale LLMs, we propose a self-correction distillation (SCD) method. In SCD, an error prompt mechanism (EPM) is designed to detect errors and provide customized error messages during inference, and a two-stage distillation strategy is designed to transfer large-scale LLMs' query-generation and error-correction capabilities to small-scale LLM. Experiments across 5 benchmarks with 3 structured data types demonstrate that our SCD achieves the best performance and superior generalization on small-scale LLM (8B) compared to other distillation methods, and closely approaches the performance of GPT4 on some datasets. Furthermore, large-scale LLMs equipped with EPM surpass the state-of-the-art results on most datasets.

翻译：结构化数据问答（QA），包括表格问答、知识图谱（KG）问答以及时序知识图谱问答，是一个关键的研究领域。大型语言模型（LLM）的进展推动了如TrustUQA等统一结构化问答框架的显著进步。然而，这些框架在应用于小规模LLM时面临挑战，因为小规模LLM在生成结构化查询时容易出错。为提升小规模LLM的结构化数据问答能力，我们提出了一种自校正蒸馏（SCD）方法。在SCD中，设计了一种错误提示机制（EPM），用于在推理过程中检测错误并提供定制化的错误信息，同时设计了一种两阶段蒸馏策略，将大规模LLM的查询生成和错误校正能力迁移到小规模LLM。在涵盖3种结构化数据类型的5个基准测试上的实验表明，与其他蒸馏方法相比，我们的SCD在小规模LLM（8B）上实现了最佳性能和卓越的泛化能力，并在部分数据集上接近GPT4的表现。此外，配备EPM的大规模LLM在大多数数据集上超越了现有最优结果。