arXiv:2606. 04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users.
By Yassir El Attar, Esra D\"onmez, Maximilian Maurer, Agnieszka Falenska
arXiv:2606. 04199v1 Announce Type: cross Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies.
By Aya Vera-Jimenez, Samuel Jaeger, Calvin Ibenye, Dhrubajyoti Ghosh
arXiv:2504.11952v4 Announce Type: replace-cross
Abstract: An ideal detection system for machine generated content is supposed to work well on any generator as many more advanced LLMs come into existe...
By Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Kanwal Mehreen, Drishti Sharma, Siddhant Gupta, Jebish Purbey, Ashay Srivastava, Subhasya TippaReddy, Arvind Reddy Bobbili, Suraj Telugara Chandrashekhar, Ankan Ghosh, Modabbir Adeeb, Srinadh Vura, Suman Debnath, Hamza Farooq
The paper examines how fine‑tuned Transformer detectors (DistilBERT, RoBERTa) perform on an out‑of‑distribution dataset of hyper‑local news generated by LLMs. Using SHAP‑based explainable AI, the authors uncover three phenomena: a Call‑to‑Action bias, a Specificity Bias where hallucinated named entities evade detection, and a Wikipedia Paradox where formal human prose is misclassified as AI. These results highlight systematic shortcomings of purely statistical detectors in structured human domains and underscore the need for semantically‑aware, interpretable detection methods.
By Nico Morloher, Johannes Reschke, Mohamed Mady
The paper introduces “KoLing”, a bias‑controlled adversarial benchmark of 50.9k Korean instances designed to suppress superficial cues while preserving semantic content, and evaluates both LLM‑generated and LLM‑modified text detection. It also proposes “KoLingProbe”, an interpretable detector grounded in theoretical linguistics that models Korean‑specific properties across multiple linguistic levels. Experiments demonstrate that KoLingProbe outperforms strong baselines on all detection tasks and remains robust to adversarial attacks, with analyses showing that integrating complementary linguistic features across multiple levels improves both performance and interpretability.
By SungHo Kim, SangKeun Lee
The paper examines how fine‑tuned Transformer detectors (DistilBERT, RoBERTa) perform on an out‑of‑distribution dataset of hyper‑local news generated by LLMs. Using SHAP‑based explainable AI, it identifies three phenomena: a reliance on a “Call‑to‑Action” syndrome, a “Specificity Bias” where hallucinated named entities evade detection, and a “Wikipedia Paradox” where formal human text is misclassified as AI. These results highlight systematic shortcomings of purely statistical detection methods in structured human domains.