IndicDetect is a benchmark for evaluating AI‑generated text detection in Hindi, Telugu, and Tamil. It pairs curated human‑written texts with LLM‑generated counterparts across multiple domains and generators, testing detectors under domain shift, generator shift, and adversarial perturbation. The study shows that supervised neural detectors fail to generalize to unseen generators and attacks, with Hindi experiencing the greatest degradation, indicating that robustness—not peak accuracy—is the main weakness in Indic language detectors.
By Bhaskar Ganesh Devalla, Junchao Wu, Nilesh Dokuparthi, Greeshma Yaluru, Tatiana Muniz Rodriguez, Lidia S. Chao, Derek F. Wong
LLMTrace is a new large‑scale bilingual (English and Russian) corpus designed to improve AI‑written text detection. It contains character‑level annotations that enable precise localization of AI‑generated segments, supporting both full‑text binary classification and interval detection tasks. The dataset is built from a diverse set of modern proprietary and open‑source LLMs to address gaps in existing resources, such as outdated models, limited language coverage, and lack of mixed human‑AI authorship data.
By Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Maksim Kuprashevich
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 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
arXiv:2610.15795v1 Announce Type: cross
Abstract: Automated moderation systems are deployed at scale, yet they remain opaque to the users and moderators affected by their decisions, and their coverag...
By Muhammad Shahid Iqbal Malik
The paper introduces a new benchmark for Urdu‑to‑English idiomatic translation, featuring 4,000 manually verified sentence pairs in both native Perso‑Arabic script and Romanized Urdu. It evaluates multiple tasks—translation, paraphrasing, idiom span detection, and back‑translation—using various prompting strategies, and finds that state‑of‑the‑art large language models outperform traditional neural machine translation systems, especially in preserving figurative meaning. The study also highlights challenges posed by the lack of standardized orthography in Romanized Urdu, which affects consistency and idiom span detection.
By Muhammad Farmal Khan, Mousumi Akter