基于框架的合成声明生成用于利用大规模表格数据的自动事实核查
Frame-Guided Synthetic Claim Generation for Automatic Fact-Checking Using High-Volume Tabular Data
AI总结：
本文提出基于框架的合成声明生成方法，利用大规模表格数据提升自动事实核查的准确性与挑战性。
摘要：
自动化事实核查基准测试大多忽略了将声明与现实世界、大量结构化数据验证的挑战，而是专注于小规模、精心整理的表格。我们介绍了一个新的大规模、多语言数据集来解决这一关键缺口。它包含78,503个基于434个复杂OECD表格的合成声明，每个表格平均超过50万行。我们提出了一种新的、基于框架的方法，其中算法根据六个语义框架自动选择重要的数据点，以生成英文、中文、西班牙语和印地语的逼真声明。关键在于，我们通过知识探测实验证明，LLMs并未记住这些事实，迫使系统进行真正的检索和推理，而不是依赖参数化知识。我们提供了一个基线SQL生成系统，并展示了我们的基准测试极具挑战性。我们的分析发现，证据检索是主要瓶颈，模型在大规模表格中难以找到正确数据。该数据集为解决这一未解决的现实问题的研究提供了关键新资源。
英文摘要：
Automated fact-checking benchmarks have largely ignored the challenge of verifying claims against real-world, high-volume structured data, instead focusing on small, curated tables. We introduce a new large-scale, multilingual dataset to address this critical gap. It contains 78,503 synthetic claims grounded in 434 complex OECD tables, which average over 500K rows each. We propose a novel, frame-guided methodology where algorithms programmatically select significant data points based on six semantic frames to generate realistic claims in English, Chinese, Spanish, and Hindi. Crucially, we demonstrate through knowledge-probing experiments that LLMs have not memorized these facts, forcing systems to perform genuine retrieval and reasoning rather than relying on parameterized knowledge. We provide a baseline SQL-generation system and show that our benchmark is highly challenging. Our analysis identifies evidence retrieval as the primary bottleneck, with models struggling to find the correct data in massive tables. This dataset provides a critical new resource for advancing research on this unsolved, real-world problem.
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。
