用合成数据提升知识图谱问答的准确率，自动生成并验证语义等价的问法。
Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answering
- 用大模型生成查询的精准自然语言改写，再用学习型验证器判断语义是否一致。
- 在LC-QuAD 2.0上验证，合成数据与人工标注高度一致，优于传统评估指标。
- 适合需要高质量问答数据的系统开发者和知识图谱研究者。
问答系统需精准对齐用户问题与结构化查询，但高质量查询-自然语言（Q-NL）配对稀缺。为此，我们提出Q-NL Verifier，利用大语言模型生成语义精确的查询自然语言改写，并引入可学习的验证组件，自动判断生成改写与原查询是否语义等价。在知名基准LC-QuAD 2.0上的实验表明，该方法能良好泛化至其他模型生成及人工翻译的改写，在不同复杂度查询下均与人类判断高度一致，且显著优于现有NLP评估指标。将验证器集成进问答流水线后，经筛选的合成数据在翻译正确性上明显提升，推动自然语言到查询的转换准确率。最后，我们发布了更新版的LC-QuAD 2.0数据集，包含合成的Q-NL对与验证分数，为鲁棒、可扩展的问答系统提供新资源。
原文摘要 · Abstract (English)
Question answering (QA) requires accurately aligning user questions with structured queries, a process often limited by the scarcity of high-quality query-natural language (Q-NL) pairs. To overcome this, we present Q-NL Verifier, an approach to generating high-quality synthetic pairs of queries and NL translations. Our approach relies on large language models (LLMs) to generate semantically precise natural language paraphrases of structured queries. Building on these synthetic Q-NL pairs, we introduce a learned verifier component that automatically determines whether a generated paraphrase is semantically equivalent to the original query. Our experiments with the well-known LC-QuAD 2.0 benchmark show that Q-NL Verifier generalizes well to paraphrases from other models and even human-authored translations. Our approach strongly aligns with human judgments across varying query complexities and outperforms existing NLP metrics in assessing semantic correctness. We also integrate the verifier into QA pipelines, showing that verifier-filtered synthetic data has significantly higher quality in terms of translation correctness and enhances NL to Q translation accuracy. Lastly, we release an updated version of the LC-QuAD 2.0 benchmark containing our synthetic Q-NL pairs and verifier scores, offering a new resource for robust and scalable QA.
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