自然语言不适合完全替代编程等正式语言，两者应协同使用。
Position: Natural Language Should Not Fully Replace Formal Languages
- 提出任务特异性概念，衡量语言表达需求的精确程度
- 证明存在阈值：超特定任务时，用自然语言描述成本高于直接写正式代码
- 适合需要灵活沟通或高精度控制的场景，如生成、编程和音频创作
大型语言模型的兴起促使人们认为自然语言可完全取代编程等正式语言。本文指出，这种观点忽视了自然语言在开放语境中优化模糊表达的本质特性。我们提出以‘任务特异性’为核心的理论框架，定义为给定用户需求后对输出空间（如所有可能图像）不确定性信息论的降低。我们证明了‘特异性交叉定理’，表明存在一个临界点，超过该点将正式要求转化为自然语言的表达成本将超过直接形式化表达的成本。通过跨模态案例研究（如图像生成、代码合成、音频制作），我们发现自然语言在低特异性任务中表现优异，而正式语言在高要求任务中更具优势。结论是：自然语言与正式语言应互补，建议发展可跨越特异性谱系的混合系统。
原文摘要 · Abstract (English)
Recent advances in large language models and their widespread adoption have prompted claims that natural language could entirely replace formal languages, such as programming languages for software design. In this position paper, we argue that this perspective overlooks fundamental linguistic properties of natural language, specifically that it is optimized for underspecification in open-ended contexts. We introduce a formal framework centered on *task specificity*, defining it as the information-theoretic reduction of uncertainty in an output space -- such as all possible images -- given a user's specific requirements. We prove a *specificity crossover theorem*, showing the existence of a threshold beyond which the cost to express formal requirements into natural language exceeds the cost of direct formal specification. By analyzing case studies across modalities, such as image generation, code synthesis, and audio production, we demonstrate that natural language excels at low specificity tasks, while formal languages are advantageous on tasks with stricter requirements. We conclude that natural and formal languages are complementary tools and advocate the development of hybrid systems that allow users to move across the specificity spectrum.
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