ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline

ACL ARR 2026 January Submission4173 Authors

Keywords: interactive and collaborative generation, inference methods, conlangs, creativity, NLP, LLMs

Abstract: 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. We will release our code and data.

Paper Type: Long

Research Area: Natural Language Generation

Research Area Keywords: interactive and collaborative generation, inference methods

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: constructed languages, English (used for prompting)

Submission Number: 4173