Interpretable Text Classification Applied to the Detection of LLM-generated Creative Writing

• URL: http://arxiv.org/abs/2601.07368v1
• Date: Mon, 12 Jan 2026 09:50:15 GMT
• Title: Interpretable Text Classification Applied to the Detection of LLM-generated Creative Writing
• Authors: Minerva Suvanto, Andrea McGlinchey, Mattias Wahde, Peter J Barclay,
• Abstract summary: We consider the problem of distinguishing human-written creative fiction (excerpts from novels) from similar text generated by an LLM.<n>Our results show that, while human observers perform poorly (near chance levels) on this binary classification task, a variety of machine-learning models achieve accuracy in the range 0.93 - 0.98.
• Score: 0.20999222360659608
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We consider the problem of distinguishing human-written creative fiction (excerpts from novels) from similar text generated by an LLM. Our results show that, while human observers perform poorly (near chance levels) on this binary classification task, a variety of machine-learning models achieve accuracy in the range 0.93 - 0.98 over a previously unseen test set, even using only short samples and single-token (unigram) features. We therefore employ an inherently interpretable (linear) classifier (with a test accuracy of 0.98), in order to elucidate the underlying reasons for this high accuracy. In our analysis, we identify specific unigram features indicative of LLM-generated text, one of the most important being that the LLM tends to use a larger variety of synonyms, thereby skewing the probability distributions in a manner that is easy to detect for a machine learning classifier, yet very difficult for a human observer. Four additional explanation categories were also identified, namely, temporal drift, Americanisms, foreign language usage, and colloquialisms. As identification of the AI-generated text depends on a constellation of such features, the classification appears robust, and therefore not easy to circumvent by malicious actors intent on misrepresenting AI-generated text as human work.

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