用多模态知识图谱生成高质量推理数据，提升小样本模型表现
MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs
- 基于多模态知识图谱细粒度提取知识，支持路径自定义采样
- 在14,950条数据上微调大模型，推理准确率提升9.2%
- 适合构建复杂基准测试，尤其对表格与公式类任务有挑战性
高质量训练数据的合成对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、效果验证和可解释性方面存在局限，基于知识图谱的方法仍缺乏功能多样性、粒度精细度、可定制性与评估体系。为此，我们提出MMKG-RDS框架，利用多模态知识图谱实现推理数据合成，支持细粒度知识提取、路径自定义采样及多维度数据质量评分。通过构建MMKG-RDS-Bench数据集（涵盖5个领域、17种任务类型、14,950个样本）进行验证，实验表明，在少量合成数据上微调Qwen3系列模型（0.6B/8B/32B），可使推理准确率提升9.2%。该框架生成的数据具有多样性，能有效挑战现有模型在表格与公式任务上的表现，适用于复杂基准建设。代码与数据已开源。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。