通过条件推理，识别复杂事件中影响结果的隐藏因素。
CoRE: Condition-based Reasoning for Identifying Outcome Variance in Complex Events
- 基于目标与状态数据构建条件推理任务
- 大模型在缺失上下文时更依赖条件判断结果
- 适合研究因果推理与模型可信度的读者
理解哪些潜在条件导致特定结果，有助于批判性评估复杂事件的结论。识别隐含条件并分析其对结果的影响极具挑战。本文结合两个现有数据集的目标与状态标注，构建条件推理任务，探索条件对结果的影响。我们测试了不同规模和意图对齐程度的开闭源大模型，发现当上下文不完整时，条件信息能有效辅助判断。模型在生成和识别结果变异条件方面表现差异显著，影响其在结果验证中的表现。例如，GPT-4o 等大模型在约束较少的情况下更为谨慎。
原文摘要 · Abstract (English)
Knowing which latent conditions lead to a particular outcome is useful for critically examining claims made about complex event outcomes. Identifying implied conditions and examining their influence on an outcome is challenging. We handle this by combining and augmenting annotations from two existing datasets consisting of goals and states, and explore the influence of conditions through our research questions and Condition-based Reasoning tasks. We examine open and closed LLMs of varying sizes and intent-alignment on our reasoning tasks and find that conditions are useful when not all context is available. Models differ widely in their ability to generate and identify outcome-variant conditions which affects their performance on outcome validation when conditions are used to replace missing context. Larger models like GPT-4o, are more cautious in such less constrained situations.
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