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is built from a diverse set of modern proprietary and open‑source LLMs to address gaps in existing resources, such as outdated models, limited language coverage, and lack of mixed human‑AI authorship data.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:2610.22940v1 Announce Type: new
Abstract: As large language models (LLMs) generate increasingly fluent text, the need for reliable detection of LLM-generated content has grown substantially. Ho...
By SungHo Kim, SangKeun Lee
arXiv:2609.22138v1 Announce Type: cross
Abstract: Hidden-state hallucination probing - training a linear classifier on an LLM's internal activations to detect whether a generated answer is faithful t...
By Tanveer Singh (Plaksha University)
IndicTriMix presents a new benchmark and models for token‑level language identification in tri‑language code‑mixed text involving Hindi, Gujarati, and Bengali. The authors reformulate the task as sequence labeling and fine‑tune transformer models MuRIL and XLM‑RoBERTa, evaluating them on manually annotated test sets. They also introduce two code‑mixing generation methods using parallel sentences and release the datasets and fine‑tuned models for public use.
By Pruthwik Mishra, Rudra Trivedi, Avi Patel, Ashok Urlana, Shrikant Malviya
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
VietAIDetector is an open‑source, zero‑shot tool for detecting Vietnamese AI‑generated text. It offers a Gradio web interface that accepts raw Vietnamese text, common file formats, scanned documents, and very long texts beyond typical LLM context limits. Built on a Vietnamese‑specific language model, it outperforms existing English‑centric methods on out‑of‑domain datasets and lets users choose detection thresholds based on F1, accuracy, or TPR@0.05FPR, with results viewable or downloadable as a PDF report.
By Trieu Hai Nguyen, Van-Dung Hoang