用大模型分步造新语言，无需专业背景也能生成多样且自洽的虚构语言。
ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline
- 分五步走：音系、形态、句法、词汇、翻译，每步用大模型推理并自我优化。
- 自动与人工评估均证明能生成结构一致、类型多样的虚构语言。
- 适合对语言设计、创意生成感兴趣的开发者或艺术家。
虚构语言（conlangs）如世界语和昆雅语在艺术、哲学和国际交流中扮演重要角色。与此同时，基础模型已彻底改变文本、图像等领域的创造性生成。本文利用现代大模型作为计算创造力助手，实现端到端的虚构语言创建。我们提出 ConlangCrafter，一个分步多跳的流水线，将语言设计分解为音系、形态、句法、词汇生成和翻译五个模块化阶段。在每个阶段，方法利用大模型的元语言推理能力，通过注入随机性促进多样性，并借助自反馈机制提升语言描述的一致性。我们构建了一个新颖、可扩展的评估框架，涵盖一致性与类型多样性指标。自动与人工评估均表明，ConlangCrafter 能在无须人类语言学专业知识的情况下，生成连贯且多样的虚构语言。
原文摘要 · Abstract (English)
Constructed languages (conlangs) such as Esperanto and Quenya have played diverse roles in art, philosophy, and international communication. Meanwhile, foundation models have revolutionized creative generation in text, images, and beyond. In this work, we leverage modern LLMs as computational creativity aids for end-to-end conlang creation. We introduce ConlangCrafter, a multi-hop pipeline that decomposes language design into modular stages -- phonology, morphology, syntax, lexicon generation, and translation. At each stage, our method leverages LLMs' metalinguistic reasoning capabilities, injecting randomness to encourage diversity and leveraging self-refinement feedback to encourage consistency in the emerging language description. We construct a novel, scalable evaluation framework for this task, evaluating metrics measuring consistency and typological diversity. Automatic and manual evaluations demonstrate ConlangCrafter's ability to produce coherent and varied conlangs without human linguistic expertise.
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