Introduction

The rapid development of large language models (LLMs) has given rise to a series of challenges, including the generation of disinformation, the spread of harmful content, and various forms of misuse. Against this backdrop, the efficient discrimination between LLM-generated text and human-written text has become an urgent and critical research issue in the field of natural language processing (NLP). While remarkable progress has been made, relevant research has largely focused on English, systematic and technical exploration for the Chinese remain scarce. This shared task aims to fill this gap, build more robust Chinese LLM-generated text detectors, and advance research and real-world applications in this field within the Chinese.

Following the success of the 1st Shared Task on LLM-Generated Text Detection (NLPCC 2025), the 2nd Shared Task on LLM-Generated Text Detection in 2026 features significant upgrades: the task formulation has been expanded from binary to ternary classification. Specifically, in addition to distinguishing between human-written text and LLM-generated text, a new category for identifying LLM-refined text has been introduced, which better aligns with real-world application scenarios of LLMs. Participating teams are required to design and implement text detection algorithms based on the training data provided to achieve accurate classification and will undergo rigorous stress testing.

Tentative Schedule

Organizers

This shared task is jointly organized by the Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory (NLP2CT) at the University of Macau, Central China Normal University, and Alibaba Cloud.

• Junchao Wu | University of Macau （Contact）nlp2ct.junchao@gmail.com | Homepage
• Derek Fai Wong | University of Macau | Homepage
• Runzhe Zhan | University of Macau | Homepage
• Zeyu Wu | University of Macau
• Zhiwen Xie | University of Macau / Central China Normal University
• Yichao Du | Alibaba Cloud
• Longyue Wang | Alibaba Cloud | Homepage