AI Model Creates Conlangs Beyond Human Imagining

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…