ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline
Summary
ConlangCrafter is a multi-hop LLM pipeline that automates constructed language (conlang) creation by decomposing the process into modular stages including phonology, morphology, syntax, lexicon generation, and translation. The system leverages LLMs' metalinguistic reasoning with randomness injection and self-refinement to produce coherent and typologically diverse constructed languages.
View Cached Full Text
Cached at: 04/20/26, 08:31 AM
# ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline Source: https://arxiv.org/abs/2508.06094 View PDF (https://arxiv.org/pdf/2508.06094) > 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. ## Submission history From: Morris Alper [view email (https://arxiv.org/show-email/b54f65b4/2508.06094)] **[[v1]](https://arxiv.org/abs/2508.06094v1)** Fri, 8 Aug 2025 07:36:48 UTC (1,348 KB) **[[v2]](https://arxiv.org/abs/2508.06094v2)** Thu, 9 Oct 2025 22:34:49 UTC (1,272 KB) **[[v3]](https://arxiv.org/abs/2508.06094v3)** Thu, 22 Jan 2026 13:54:42 UTC (1,433 KB) **[v4]** Fri, 17 Apr 2026 16:51:16 UTC (1,423 KB)
Similar Articles
Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
This tutorial paper provides an overview of building multilingual and multimodal LLMs for low-resource languages, covering data creation, model alignment, fine-tuning, and evaluation, with a focus on practical recipes and hands-on resources.
Build a LLM from Scratch using MLX
A guide on building a large language model from scratch using Apple's MLX framework.
When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
This paper introduces Communicative Language Symbolism Routing (CLSR), where multiple LLM agents autonomously invent and evolve compact symbolic languages for reasoning, achieving 3-6x token reduction over chain-of-thought while maintaining accuracy.
LLMBridge: An LLM Pipeline for End-to-end Referential Bridging Resolution in English
LLMBridge introduces an LLM-based pipeline for end-to-end referential bridging resolution, achieving state-of-the-art performance on three English datasets. The system combines heuristic pre/post-processing with LLM natural language inference.
Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models
This paper studies how humans and large language models linguistically accommodate each other during multi-turn conversations, finding that LLMs overconverge to user style while humans accommodate LLMs no differently than humans.