AI 中文总结
研究专利权利要求生成中CoT提示是否有益，提出特定任务CoT方法并评估。结果显示推理增强提示可提升权利要求质量，且隐式CoT优于显式CoT，显式CoT会引入信息瓶颈，为法律任务和CoT应用提供新见解。
AI 中文摘要
专利权利要求起草是一项具有挑战性的法律起草任务，需要技术专长、精确的语言控制、严格遵循形式惯例以及保留权利要求要素之间复杂的逻辑关系。虽然思维链（CoT）提示已被广泛用于提高大语言模型（LLMs）的推理能力，但近期证据表明，在高度结构化或模式敏感任务中其益处可能有限甚至负面。因此，本文研究CoT提示对专利权利要求生成是否有益。我们提出一种针对专利权利要求生成的特定任务CoT方法，并通过自动指标和人类专家评估来评估其有效性。结果表明，推理增强提示可提高权利要求质量。此外，我们展示了一个反直觉但重要的实证发现：推理保持内部化而非明确语言表达的隐式CoT始终优于显式CoT。通过系统分析，我们表明显式CoT会为权利要求生成引入不必要的信息瓶颈。语言化推理可能通过三种特定机制损害最终输出质量：关键细节的抽象、内化生成模式的破坏以及级联错误传播。我们的发现为法律任务和CoT应用提供了新见解。
英文摘要
Patent claim drafting is a challenging legal drafting task that requires technical expertise, precise linguistic control, strict adherence to formal conventions, and the preservation of complex logical relationships among claim elements. While Chain-of-Thought (CoT) prompting has been widely used to improve the reasoning capabilities of large language models (LLMs), recent evidence suggests that its benefits may be limited, or even negative, in highly structured or pattern-sensitive tasks. Therefore, this paper investigates whether CoT prompting benefits patent claim generation. We propose a task-specific CoT method for patent claim generation and evaluate its effectiveness through both automatic metrics and human expert assessment. Our results show that reasoning-enhanced prompting can improve claim quality. Moreover, we demonstrate a counter-intuitive but important empirical finding: implicit CoT, where reasoning is kept internal rather than explicitly verbalized, consistently outperforms explicit CoT. Through systematic analysis, we show that explicit CoT can introduce an unnecessary information bottleneck for claim generation. Verbalized reasoning may compromise the quality of final outputs through three specific mechanisms: abstraction of critical details, disruption of internalized generation patterns, and cascading error propagation. Our findings provide new insights into legal tasks and CoT applications.
CommentsAccepted to AI for Law Workshop @ ICML 2026
