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This glossary entry is a conceptual framework for linguists who use model-generated text as research data, separating broad corpus-like "bush" collections from tightly controlled "garden" elicitation experiments. Its main point is that synthetic text is a distinct object of study, not a stand-in for natural language, so findings depend on how the outputs were generated. Researchers using LLM outputs as proxies for human language should treat generation conditions as part of the data.
Sources coveringarXiv, Roboflow Blog

Synthetic linguistic data

arXiv2 min read

arXiv:2610.34215v1 Announce Type: new Abstract: Synthetic linguistic data have become increasingly prominent in linguistic research, from the analysis of large collections of AI-generated texts to controlled experiments investigating grammatical and lexical preferences. This glossary entry extends Bresnan's (2016) distinction between the linguistic garden and the bush by introducing an artificial plant nursery as a metaphor for synthetic data. We distinguish between synthetic bush data, consisting primarily of corpus-like collections of model-generated language, and synthetic garden data, in which model outputs are elicited under controlled conditions to investigate specific linguistic hypotheses. We argue that synthetic data constitute a distinct object of linguistic investigation: comparing them with natural language data can reveal both shared patterns and divergences, but their interpretation requires careful consideration of the conditions under which they are generated.

Abstract:Synthetic linguistic data have become increasingly prominent in linguistic research, from the analysis of large collections of AI-generated texts to controlled experiments investigating grammatical and lexical preferences. This glossary entry extends Bresnan's (2016) distinction between the linguistic garden and the bush by introducing an artificial plant nursery as a metaphor for synthetic data. We distinguish between synthetic bush data, consisting primarily of corpus-like collections of model-generated language, and synthetic garden data, in which model outputs are elicited under controlled conditions to investigate specific linguistic hypotheses. We argue that synthetic data constitute a distinct object of linguistic investigation: comparing them with natural language data can reveal both shared patterns and divergences, but their interpretation requires careful consideration of the conditions under which they are generated.

Computation and Language (cs.CL)

arXiv:2610.34215 [cs.CL]

(or arXiv:2610.34215v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.34215 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 219 words

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