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AI is now building its own languages

Researchers have developed ConlangCrafter, a tool designed to generate entirely new, consistent languages. While creating a language—a practice known as conlanging—is usually a human endeavor for fiction, this AI model reaches beyond human patterns to invent non-traditional communication systems. It offers a new way for linguists to test how language structure impacts artificial intelligence, potentially helping us understand the connection between how we speak and how we think.

Edition № 117Room: Explainer27 June 20261 min readSources: 1
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For decades, building a new language—a 'conlang'—was the domain of devoted writers crafting dialects for fictional civilizations like the Klingons or Dothraki. Now, a new model called ConlangCrafter can automate the process, generating complex, rule-based languages that often look very different from the ones humans have created.

Researchers at UC Berkeley, Carnegie Mellon, and Tel Aviv University recently published findings showing that ConlangCrafter can generate diverse, consistent languages at a higher success rate than general-purpose large language models. It handles everything from phonology, which is the organization of sounds, to morphosyntax, the way words fit together into sentences.

The architecture of a made-up language

ConlangCrafter functions as a specialized system designed to maintain internal logic while introducing variety. It uses a random number generator to ensure each output is unique, while a built-in editing loop acts as an automated editor to scrub any contradictions in the language's grammar or rules. Think of it less like a standard chatbot and more like a logic engine that enforces a strict set of linguistic constraints. It can even generate unconventional communication systems, such as a language based on colors and gestures, specifically designed to model how non-human intelligence might convey information.

For linguists and AI researchers, this is a practical tool for experimentation rather than just a way to build fantasy worlds. Currently, researchers struggle to study how different linguistic structures affect how well an AI performs on tasks. By generating bespoke languages with specific properties, scholars can finally run controlled tests on how vocabulary and sentence structure influence model behavior. Ultimately, this work hints at a bigger goal: creating simulated societies with unique languages to see if those communication patterns truly shape how an intelligence perceives the world, testing the long-standing Sapir-Whorf hypothesis in a digital environment.

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