EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection

Detecting LLM-Generated Tokens in Human-LLM Coauthored Text

The key idea is to smooth adjacent token scores to reduce their variability, while using an adaptive Lepski-type rule to select the bandwidth according to the local authorship structure, and the proposed method achieves favorable mean square error performance in estimating the underlying signal.

Information Entropy for LLM-generated Text Detection

A novel method named IED is proposed, which leverages the information gain to construct the vector of which each dimension represents the information entropy of each word, and then adopts a classifier to conduct the detection.

Munich_Z@GermEval Shared Task 2025:When Prompting Is Not Enough: The Limits of Large Language Models in GermEval’s 2025 Harmful Content Detection Task

This work evaluates prompting strategies for subtask 2 of the GermEval 2025 Harmful Content Detection challenge, which involves classifying whether a tweet attacks the free democratic basic order and shows that techniques such as Chain-of-Thought, In-Context Learning or Task Decomposition outperform approaches like Task Description.

Advancing Machine-generated Text Detection: A Comprehensive Evaluation of Transformer-based Models

Test set results show that Decoding-Enhanced Bert with Disentangled Attention (DeBERTa) achieves the highest macro F1 − Score of 85.48%, surpassing the previously top-ranked Multi-Task Learning (MTL) system, which attains a macro F1 of 83.07%.

LLM-generated scientific content detection method

Cost-Aware Human-LLM Collaboration for Post-OCR Corrections in Swiss Historical Newspapers

A regression-guided routing approach that prioritizes segments by predicted CER improvement, paired with a safeguard layer that detects harmful LLM corrections and routes uncertain segments to human review, and substantially outperforms standard confidence-based approaches is introduced.