基于多模态知识图谱的推理数据合成：通过深度挖掘多模态知识图谱进行推理数据合成
MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs
AI总结：
MMKG-RDS通过深度挖掘多模态知识图谱，提出灵活的推理数据合成框架，提升模型推理能力并生成高质量数据。
摘要：
合成高质量的训练数据对于提升领域模型的推理能力至关重要。现有方法在长尾知识覆盖、有效性验证和可解释性方面存在局限。基于知识图谱的方法在功能、粒度、可定制性和评估方面仍显不足。为了解决这些问题，我们提出了MMKG-RDS，一个灵活的推理数据合成框架，利用多模态知识图谱。它支持细粒度的知识提取、可定制的路径采样和多维数据质量评分。我们通过MMKG-RDS-Bench数据集验证了MMKG-RDS，涵盖五个领域、17种任务类型和14950个样本。实验结果表明，对少量合成样本进行微调Qwen3模型（0.6B/8B/32B）可将推理准确率提高9.2%。该框架还生成了具有区别的数据，对涉及表格和公式的任务提出了挑战，对复杂基准构建具有用处。数据集和代码可在https://github.com/360AILAB-NLP/MMKG-RDS获取。
英文摘要：
Synthesizing high-quality training data is crucial for enhancing domain models' reasoning abilities. Existing methods face limitations in long-tail knowledge coverage, effectiveness verification, and interpretability. Knowledge-graph-based approaches still fall short in functionality, granularity, customizability, and evaluation. To address these issues, we propose MMKG-RDS, a flexible framework for reasoning data synthesis that leverages multimodal knowledge graphs. It supports fine-grained knowledge extraction, customizable path sampling, and multidimensional data quality scoring. We validate MMKG-RDS with the MMKG-RDS-Bench dataset, covering five domains, 17 task types, and 14,950 samples. Experimental results show fine-tuning Qwen3 models (0.6B/8B/32B) on a small number of synthesized samples improves reasoning accuracy by 9.2%. The framework also generates distinct data, challenging existing models on tasks involving tables and formulas, useful for complex benchmark construction. The dataset and code are available at https://github.com/360AILAB-NLP/MMKG-RDS
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
