Smotrom tvoja på ander drogoj verden! Resurrecting Dead Pidgin with Generative Models: Russenorsk Case Study (2025)

Abstract: Russenorsk, a pidgin language historically used in trade interactions between Russian and Norwegian speakers, represents a unique linguistic phenomenon. In this paper, we attempt to analyze its lexicon using modern large language models (LLMs), based on surviving literary sources. We construct a structured dictionary of the language, grouped by synonyms and word origins. Subsequently, we use this dictionary to formulate hypotheses about the core principles of word formation and grammatical structure in Russenorsk and show which hypotheses generated by large language models correspond to the hypotheses previously proposed ones in the academic literature. We also develop a “reconstruction” translation agent that generates hypothetical Russenorsk renderings of contemporary Russian and Norwegian texts.

## introduction this paper is part of a relatively recent trend to use large language models (llms) for scientific discovery in various research areas, demonstrating that these natural language processing models have unprecedented generalization capacity. to our knowledge, this is the first result that demonstrates how large language models could be effectively used in a study of dead contact languages. we first provide a brief overview of llm-driven scientific discovery and then dive deeper into russenorsk and its properties. ## new russenorsk vocabulary we present a new, extensive russenorsk vocabulary based on a variety of sources available in digital form. to compile a russenorsk vocabulary, we began by extracting lemmas from two primary sources: the online repository at https://en.wiktionary.org/wiki/category: russenorsk_lemmas and digitized version of @xcite . this process yielded a csv file with 691 entries. each entry encompasses: after assembling the initial dictionary, we employed a pipeline (based on a sonnet-driven system) to cluster entries with similar morphology, and subsequently grouped these by semantic proximity. this reformatting required manual review over several days to remove erroneous groupings and "hallucinated" entries. finally, the cleaned dictionary was reviewed by additional collaborators, who discovered a few remaining mistakes that were subsequently corrected by hand. figure 1 provides an example of a vocabulary entry. full vocabulary is available online foot_1 . ## linguistic discovery assistant using the constructed vocabulary as input we developed a linguistic discovery assistant. we propose a pipeline for hypothesis generation that leverages a large language model (llm) as an exploratory tool for analyzing russenorsk linguistic phenomena. ## results of agentic discovery the agent presented in section 3 produced the following key findings: a quick qualitative scan of our corpus suggests that both svo and sov clauses occur with comparable frequency, with svo only slightly more common overall. the persistence of sov remains noteworthy, however, because that order is not characteristic of either norwegian or russian. claude/sonnet achieves marginally higher recall than o1. at the same time, the union of their outputs provides broader coverage than either model individually, underscoring a useful cumulative effect when both systems are consulted in parallel. ## "reconstruction" translation for extinct languages the final component of our study demonstrates how the pipeline introduced in section 3 can be extended to "reconstruct" translations for a contact language no longer in active use. russenorsk serves here as a case study, illustrating the potential for systematic alignment of parallel text, lexical matching, and informed linguistic hypotheses to recover and interpret historical content. although this approach is not strictly falsifiable, it offers a practical methodology for scholars working with extinct or endangered languages. the prompts for such a "reconstruction" translation are published online foot_5 . ## conclusion this paper demonstrates how modern large language models could be used in linguistics of lowresource dead languages and pidgins. we create the first structured dictionary of russenorsk and make it available under an open-source license. the lexicon primarily consists of borrowed terms from russian and norwegian, with occasional influences from other languages. the distribution of terms reflects the functional nature of the pidgin as a trade language. we also publish a linguistic discovery agent and demonstrate its capability to generate viable linguistic hypotheses about russenorsk. we believe similar ideas could be applied to other dead or low-resource pidgin languages. finally, we provide the first automated pipeline for "reconstructive" translation into russenorsk. russenorsk ae god språk for folk. vi snakke russisk, norsk, og litt mer, for handel og vennskap. det viser hvordan folk kan lage nye språk sammen, lett og rask.