用时间图谱计算法律文件截止日期，准确率比纯语言模型高得多。
Time as Structure: Temporal Dependency Graphs for Verifiable Deadline Computation over Legal Documents
- 构建事件时间依赖图，用校准日历引擎算截止日
- 在英国劳工上诉庭判例中准确率达90.2%，远超模型直接回答
- 适合需要精确时间推理的法律自动化场景
错过提交截止日一天，案件即被驳回，无论理由多充分。但计算截止日往往复杂：期限从触发事件起算，按法定惯例计数，且可能因强制调解期而暂停。我们探讨语言模型是否应直接回答此类问题，还是读取文档后将计算交由代码处理。本文提取带日期的事实及其依赖关系，构建时间依赖图，并通过校准日历引擎计算截止日。在英国就业上诉法庭判例中，该引擎复现了七起中的六起时效裁定，与法官判定日期一致至天。四种语言模型中最强者，虽算对时间却答错结果：在二十一份回应中，有六次其结论与其自身推理矛盾，且所有矛盾均倾向误判逾期申请为及时。为大规模测试，我们将解雇日期跨过法定边界生成427个案例，答案由计算得出而非人工标注。在两者均作答的案例中，本方案正确率达90.2%，直接回答仅61.2%。错误主要来自事件抽取，而非算术错误；在合同案例中，算术几乎无误，但常选错起算事件。
原文摘要 · Abstract (English)
Miss a filing deadline by one day and the claim is barred, however strong the case. Computing that deadline is rarely simple: the period runs from a triggering event, is counted by a statutory convention, and may be suspended by a mandatory conciliation window. We ask whether a language model should answer such questions directly, or read the document and leave the arithmetic to code. We extract dated facts and their dependencies into a temporal dependency graph and compute deadlines from it with a calendar-correct engine. On UK Employment Appeal Tribunal judgments the engine reproduces six of seven timeliness rulings, and matches the judges' own dates to the day. The strongest of four language models, asked the same cases, gets the arithmetic right and the answer wrong: in six of twenty-one responses its stated verdict contradicts its own thinking, and every contradiction runs the same way, calling a late claim timely. To test the systems at scale we move the dismissal date across the statutory boundary, generating 427 cases whose answers are computed rather than annotated. On the cases both systems answer, the pipeline is right 90.2% of the time against 61.2% for direct answering. The limit is extraction: on contracts the errors are almost never in the arithmetic, but in choosing which event the period starts from.
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