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Detecting GPT-Assisted Writing Using Interpretable Stylometric Features

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arXiv Computation and Language
Sep 23

Detecting GPT-Assisted Writing Using Interpretable Stylometric Features

The paper investigates whether stylometric features extracted only from text can distinguish GPT‑assisted writing from independently authored student work. Using data from 90 participants who produced both types of text, eight machine learning classifiers were tested, with Random Forest achieving an ROC‑AUC of 0.87 and an F1‑score of 0.84 on a held‑out test set. SHAP analysis revealed that lexical and grammatical characteristics were the primary drivers of the predictions, indicating that transparent, text‑intrinsic features can effectively signal GPT assistance.

By Rajesh Kumar, Nabeel Siddiqui, Alexander Fuchsberger
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arXiv Machine Learning
Oct 3

Sentence Specificity Scores for Collaborative Technical Documentation: A Domain-Transfer Study

The paper investigates how sentence‑specificity scores can guide the selection of revisions in collaborative technical documentation. It compares two predictors—SpeciTeller and a target‑adapted model by Ko et al.—across Wikipedia and three technical corpora, finding that the predictors rank sentences differently and that SpeciTeller can improve direction‑valid selection rates in certain datasets. The study also shows that filtering and token‑length adjustments alter but do not reconcile these ranking differences.

By Rocker D'Antonio, Thomas Benton Townsend, Dimitrios Michael Manias
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arXiv Computation and Language
Oct 5

Writerslogic at the CLEF 2026 SimpleText Track: Multi-Candidate LLM Simplification and Stacked Complexity Spotting

The Writerslogic team participated in the CLEF 2026 SimpleText shared task, tackling both text simplification (Task 1) and complexity spotting (Task 2). For simplification, they built a multi‑candidate pipeline with GPT‑4o‑mini, selecting the best candidate via a reference‑free heuristic, and their Claude Sonnet 4 submission achieved a SARI of 47.43 and BLEU of 14.21, ranking third overall on the Task 1 leaderboard. For complexity spotting, they fine‑tuned a DeBERTa‑v3‑large NLI model on 350 K labeled pairs, achieving a macro F1 of 0.8081 (0.8085 in an ensemble) on binary over‑generation identification and 0.804 accuracy on multi‑class error classification, placing them second among unique teams.

By David L. Condrey
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arXiv AI
Aug 28

Style as a Confound: False Positives in AI Detection of Non-Native Academic Writing

The study examines how professional English editing influences AI text detectors’ false-positive rates for non-native academic writing. Using 135,389 pairs of original and edited manuscripts, researchers found that detector responses varied widely—some editors increased AI scores while others decreased them—and that score changes correlated with the extent of editing. These results highlight professional editing style as a key confounding factor in AI detection, complicating the distinction between AI authorship and linguistic style.

By Hyeonchu Park, Gahye Jeong, Bugeun Kim
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arXiv Machine Learning
Sep 14

Limits of LLM Text Detectors in Education

The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.

By Lukas Gehring, Benjamin Paa{\ss}en
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