Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh

Title:Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh

Abstract:AI-text detectors are already applied to Kazakh writing, yet no study reports how often publicly available detectors flag Kazakh text whose human authorship is dated before ChatGPT. We present AINA-KK: 3,811 human passages from encyclopedic, news, web and educational pages that provably existed before ChatGPT, and 5,597 machine and gray-zone passages from 9 generators, including the Kazakh-built KazLLM and Sherkala, with script and length stress tests. Five English-trained detectors from the Hugging Face Hub flag up to 81.4% of human passages and 79.6% of published student essays; recalibrated, they catch at most 1.3% of machine text. Calibrated zero-shot detectors catch at most 50.3% of machine text from in-distribution generators. Fine-tuned encoders rank text almost perfectly, but mDeBERTa at its default threshold flags 24.4% of 50-word human excerpts, partly because machine outputs are shorter than human text; calibrated, it catches only 47.1% of KazLLM and 62.7% of Sherkala passages. Without script handling, machine-transliterated Latin-2021 text defeats every detector. Fixes that need no new data (calibration, script canonicalization, script-augmented and length-matched training, and an OR with a zero-shot scorer) form AINA-KK-Det. On test it flags 0.9% of human passages (95% CI 0.4-2.7; 1.5% on development data) and catches 85.2% of machine text from known generators, 92.9% from two unseen Gemini-family Gemma-4 models and 61.3% from the Kazakh-built ones; its configuration was chosen with test results in view. We release the data, code and detector at this https URL.

16 pages (8 main + references and appendix), 1 figure, 9 tables. Code: this https URL data: this https URL detector: this https URL

Computation and Language (cs.CL)

arXiv:2610.15870 [cs.CL]

(or arXiv:2610.15870v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.15870 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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