The Impact of Editorial Intervention on Detecting Native Language Traces

Abstract:Native Language Identification (NLI) is the task of determining an author's native language (L1) from their non-native writing. With the advent of human-AI co-authorship, learner texts are routinely corrected and rewritten by large language models, fundamentally altering the linguistic features NLI approaches depend on. In this paper, we investigate the robustness of L1 traces across increasing degrees of editorial intervention. By processing 450 essays from the Write & Improve 2024 (W&I) corpus through varying levels of grammatical error correction and paraphrasing, we demonstrate that L1 attribution does not depend solely on surface-level errors. Instead, the detection models appear to leverage deeper L1-related features, including unidiomatic lexico-semantic choices and pragmatic transfer. We find that minimal edits preserve these structural traces and maintain high L1 attribution accuracy. In contrast, fluency edits and paraphrasing normalize these L1 features, leading to a sharp decline in performance.

Computation and Language (cs.CL)

arXiv:2605.10216 [cs.CL]

(or arXiv:2605.10216v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.10216 Focus to learn more arXiv-issued DOI via DataCite

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