用语义图编织技术，从稀疏文本中可控生成复杂多跳问答。
Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesis
- 基于AMR分析构建三类语义桥，实现跨文档复杂推理路径的精准构造。
- 在四语言上提升18.3%-25.4%，生成质量超过600条人工标注数据。
- 适合需要高质量推理训练数据的研究者，尤其适用于生物医学等专业领域。
大型语言模型训练面临核心瓶颈：高质量、强推理能力的问答对稀缺，尤其在如PubMed论文或法律文件等稀疏领域。现有方法依赖表面模式，无法生成可控制的复杂多跳问题，难以检验真实理解力。本文提出首个通用框架Semantic Bridge，可从任意来源可控生成复杂多跳推理问题。其创新在于“语义图编织”——通过实体桥接（角色可变共享实体）、谓词链桥接（时间/因果/逻辑序列）和因果桥接（显式推理链）三种互补机制，系统构建跨文档复杂路径，并通过AMR驱动实现对复杂度和类型的高度控制。多模态AMR管道使往返质量提升最高达9.5%，支持生产级可控问答生成。大量评估显示，在通用数据集（Wikipedia）和专业领域（生物医学）均表现优异，四语言（英、中、法、德）平均性能提升18.3%-25.4%。从200个来源生成的问题对，仅用67%材料即超越600条人工标注。人类评估显示，问题复杂度提升23.4%，可答性提高18.7%，模式覆盖率提升31.2%。Semantic Bridge为大模型训练数据合成树立新范式，支持从稀疏源中生成目标化推理问题。核心代码与模型将开源。
原文摘要 · Abstract (English)
Large language model (LLM) training faces a critical bottleneck: the scarcity of high-quality, reasoning-intensive question-answer pairs, especially from sparse, domain-specific sources like PubMed papers or legal documents. Existing methods rely on surface patterns, fundamentally failing to generate controllable, complex multi-hop reasoning questions that test genuine understanding-essential for advancing LLM training paradigms. We present \textbf{Semantic Bridge}, the first universal framework for controllably generating sophisticated multi-hop reasoning questions from arbitrary sources. Our breakthrough innovation is \textit{semantic graph weaving}-three complementary bridging mechanisms (entity bridging for role-varying shared entities, predicate chain bridging for temporal/causal/logical sequences, and causal bridging for explicit reasoning chains)-that systematically construct complex pathways across documents, with fine-grained control over complexity and types via AMR-driven analysis. Our multi-modal AMR pipeline achieves up to 9.5% better round-trip quality, enabling production-ready controllable QA generation. Extensive evaluation demonstrates performance across both general-purpose datasets (Wikipedia) and specialized domains (biomedicine) It yields consistent 18.3%-25.4% gains over baselines across four languages (English, Chinese, French, German). Question pairs generated from 200 sources outperform 600 native human annotation examples with 67% fewer materials. Human evaluation shows 23.4% higher complexity, 18.7% better answerability, and 31.2% improved pattern coverage. Semantic Bridge establishes a new paradigm for LLM training data synthesis, enabling controllable generation of targeted reasoning questions from sparse sources. We will release our core code and semantic bridge model.
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