9 papers · 1 filter
EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection
Hongrui Bao, Hangyu Rong, Zhuoshang Wang +2
The rapid development of large language models (LLMs) has increased the need for reliable detection of LLM-generated text, especially in realistic Chinese scenarios involving human…
Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration
Hongrui Bao, Yubing Ren, Yanan Cao +3
Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational misuse, and platform governance.…
EnsemJudge: Enhancing Reliability in Chinese LLM-Generated Text Detection through Diverse Model Ensembles
Zhuoshang Wang, Yubing Ren, Guoyu Zhao +3
Large Language Models (LLMs) are widely applied across various domains due to their powerful text generation capabilities. While LLM-generated texts often resemble human-written on…
Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection
Xiaowei Zhu, Yubing Ren, Fang Fang +3
The rapid advancement of large language models has increasingly blurred the boundary between human-written and AI-generated text, raising societal risks such as misinformation diss…
WorldCup Sampling for Multi-bit LLM Watermarking
Yidan Wang, Yubing Ren, Yanan Cao +1
As large language models (LLMs) generate increasingly human-like text, watermarking has emerged as a promising solution for reliable attribution beyond mere detection. While multi-…
DNA-DetectLLM: Unveiling AI-Generated Text via a DNA-Inspired Mutation-Repair Paradigm
Xiaowei Zhu, Yubing Ren, Fang Fang +3
The rapid advancement of large language models (LLMs) has blurred the line between AI-generated and human-written text. This progress brings societal risks such as misinformation,…