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The Impact of Editorial Intervention on Detecting Native Language Traces

arXiv 2605.10216 ↗First posted 11 May 2026cs.CLcited by 0

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Abstract (arXiv)

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.

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References · 10

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  2. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  3. 2024Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challengescited by 47
  4. 2025Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasingcited by 15
  5. 2025Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authorscited by 10
  6. 2025Adapting LLMs for Minimal-edit Grammatical Error Correctioncited by 5
  7. 2024Machine Translation Models are Zero-Shot Detectors of Translation Directioncited by 3
  8. 2025Introducing OmniGEC: A Silver Multilingual Dataset for Grammatical Error Correctioncited by 3
  9. 2025Robust Native Language Identification through Agentic Decompositioncited by 1
  10. 2026Neurocomputational Mechanisms of Syntactic Transfer in Bilingual Sentence Productioncited by 1

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