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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.
arXiv AI
Sep 1
Interpretable Predictability-Based AI Text Detection: A Replication Study
arXiv:2603.15034v2 Announce Type: replace-cross Abstract: This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts. E...
arXiv AI
Jul 17
WrAFT: a Modularized Automated Writing Evaluation System for Argumentative Essays
arXiv:2607. 14524v1 Announce Type: new Abstract: This study presents WrAFT, a Writing Assessment and Feedback Tool, that delivers both accurate and reliable scores and effective comprehensive feedback to argumentative essays.
arXiv Computation and Language
Sep 22
Error-Supervised Synthetic Learner Writing for Automated Essay Scoring
arXiv:2609.23573v1 Announce Type: new Abstract: Synthetic essays can help reduce dependence on human-written data in Automated Essay Scoring (AES). However, they often lack realistic errors, limiting...
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.
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.