There are over 7,000 natural languages today, but that doesn’t stop people from occasionally making up completely new ones. These constructed languages, or conlangs, include Dothraki, Klingon, and various Elvish languages. Now, an AI model called ConlangCrafter is also capable of generating new languages—and it is particularly good at it.

In a paper published 27 June in the Proceedings of the Association of Computer Linguists, researchers analyzed ConlangCrafter’s language generation abilities, reporting that it can develop a diverse array of novel languages that consistently abide by their rules.

How ConlangCrafter Creates New Languages

In previous work, Gašper Beguš, an associate professor of linguistics at the University of California, Berkeley, showed how large language models (LLMs) can analyze languages to the same extent as most humans. In his most recent endeavour, he set out to push the language boundaries of AI models even further.

“Creating an entire language is not an easy task at all,” Beguš says, noting that some people have dedicated their careers to creating conlangs for movies, books, and video games.

But Beguš sees additional value in making AI models capable of creating truly novel languages beyond what humans could imagine. “[Models] are able to imagine or come up with things that we might not, and we can learn so much from that,” he says.

For example, ConlangCrafter can create new languages with unconventional communication systems, such as a language for a cephalopod species that uses colors and gestures instead of sounds. Of course, while this “color language” generated by ConlangCrafter isn’t truly what an octopus uses for communication, Beguš envisions these imaginary languages as a means for studying non-human centric languages in greater detail.

Beguš and the rest of the team, including Morris Alper, a postdoctoral researcher at Carnegie Mellon University and Moran Yankua, a Ph.D. student at Tel Aviv University , designed ConlangCrafter so that it can apply a wide range of linguistic rules in terms of how sounds are organized in a language (phonology), the relationship between word and sentence structure (morphosyntax), and vocabulary.

A random number generator regularly introduces variation so that every language comes out different. A built-in editing loop then reviews the result for contradictions and fixes them. Users can choose whatever mix of rules they want, or ask ConlangCrafter to make up its own rules.

“[Models] are able to imagine or come up with things that we might not, and we can learn so much from that.” —Gašper Beguš, University of California, Berkeley

“You can choose whatever flavor of language you want,” says Beguš. “You can create a mixed language between Japanese and Esperanto, for example.”

“The goal is for the languages to be creative, so they should all be different from each other,” says Alper, who specializes in multimodal machine learning and computational linguistics. “You also want them to be consistent, because a language is like a system of rules, and those rules shouldn’t contradict each other.”

To evaluate diversity, the team measured how much the generated languages differed from one another across key linguistic features such as the basic word order used in sentences. To evaluate consistency, they checked whether translations into each invented language correctly followed that language’s own rules.

They compared languages generated by ConlangCrafter to languages created by general-purpose LLMs, such as Gemini-2.5-Pro. “Our full system can be about twice as diverse and almost 70 percent more consistent than simply prompting an LLM to invent a new language,” says Alper.

ConlangCrafter in Natural Language Processing

David Mortensen, an assistant research professor at the Language Technologies Institute at Carnegie Mellon University who was not involved in the work, says that ConlangCrafter could help natural language processing researchers better evaluate the ways in which the structure of a language affects the performance of a model.

“There is a substantial body of research that suggests that linguistic structure–both at training time and test time–does affect model performance,” he says. “Hypotheses in this area have been very hard to evaluate, however.” He adds that a tool such as ConlangCrafter could help facilitate experiments on the effects of factors such as language typology and lexicon in a scientifically sound and reliable way.

ConlangCrafter is available for free online. Its creators note that the system is currently limited in more complex linguistic dimensions such as semantics, contextual and conversational use of language, and the visual aspects of writing.

Beguš envisions expanding upon this research to study the Sapir-Whorf hypothesis, which suggests that the way we speak influences the way we think and perceive the world. For example, this could involve running simulations of different worlds, each with its own language, exploring its impact on societies. “That’ll be a nice next step,” he says.