BOOST：面向程序引导事实核查的自举策略驱动推理程序生成方法
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking
AI总结：
针对程序引导事实核查中人工制作少样本演示成本高、推理程序生成原理不明的问题，提出BOOST自举方法，通过批评-精炼循环迭代优化元规则自动生成演示，提升了复杂主张验证的效果与可解释性。
摘要：
大语言模型流水线已提升了复杂主张的自动事实核查效果，但许多方法依赖少样本上下文学习，其所需的示例演示要耗费大量人力与领域专业知识。其中，程序引导推理通过将主张拆解为函数调用并执行推理程序，展现出突出潜力，但仍受限于对人工制作演示的需求。从根本上看，有效推理程序生成的底层原理仍未得到充分探究。本研究提出BOOST——一种用于自动生成少样本推理程序的自举方法。BOOST通过批评-精炼循环，迭代优化显式的数据驱动准则作为元规则来指导演示创建，无需人工干预。这实现了从零样本到少样本程序引导学习的平滑过渡，提升了可解释性与有效性。实验结果表明，在复杂主张验证任务的零样本和少样本设置下，BOOST的表现均优于此前的少样本基线方法。
英文摘要：
Large language model pipelines have improved automated fact-checking for complex claims, yet many approaches rely on few-shot in-context learning with demonstrations that require substantial human effort and domain expertise. Among these, program-guided reasoning, by decomposing claims into function calls and executing reasoning programs, which has shown particular promise, but remains limited by the need for manually crafted demonstrations. Fundamentally, the underlying principles of effective reasoning program generation still remain underexplored. In this work, we introduce BOOST, a bootstrapping approach for automated few-shot reasoning program generation. BOOST iteratively refines explicit, data-driven guidelines as meta-rules for guiding demonstration creation, using a critique-refine loop that eliminates the need for human intervention. This enables a seamless transition from zero-shot to few-shot program-guided learning, enhancing interpretability and effectiveness. Experimental results show that BOOST outperforms prior few-shot baselines in both zero-shot and few-shot settings for complex claim verification.
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