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该论文提出 ECI2D 方法，通过将语言推理链抽象为可执行程序并施加因果干预（do-operator）来筛选自生成的上下文示例，解决仅靠观察过滤示例时的观测等价问题，在数学、逻辑与常识推理基准上取得显著提升。
This paper presents an empirical study adapting the small language model Phi Silica for short-form text rewriting through dataset curation, prompt distillation, and parameter-efficient fine-tuning, showing that targeted adaptation significantly improves semantic fidelity and reduces hallucinations.