14 papers
MixDetect: Word-Level Localization and Quantification of AI Editing
Hongrui Bao, Yubing Ren, Zhendong Pan +3
Large language models are increasingly used to edit human-written text rather than generate entire texts from scratch. Conventional AI-text detectors mainly distinguish human-writt…
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.…
Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework
Zhuoshang Wang, Yubing Ren, Yanan Cao +3
While watermarking serves as a critical mechanism for LLM provenance, existing secret-key schemes tightly couple detection with injection, requiring access to keys or provider-side…
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-…
From Essence to Defense: Adaptive Semantic-aware Watermarking for Embedding-as-a-Service Copyright Protection
Hao Li, Yubing Ren, Yanan Cao +3
Benefiting from the superior capabilities of large language models in natural language understanding and generation, Embeddings-as-a-Service (EaaS) has emerged as a successful comm…
DualGuard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack
Hao Li, Yubing Ren, Yanan Cao +4
With the rapid development of cloud-based services, large language models have become increasingly accessible through various web platforms. However, this accessibility has also le…
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,…
From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework for Large Language Models
Yidan Wang, Yubing Ren, Yanan Cao +1
The rise of Large Language Models (LLMs) has heightened concerns about the misuse of AI-generated text, making watermarking a promising solution. Mainstream watermarking schemes fo…
PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization
Yidan Wang, Yanan Cao, Yubing Ren +3
Large Language Models (LLMs) excel in various domains but pose inherent privacy risks. Existing methods to evaluate privacy leakage in LLMs often use memorized prefixes or simple i…
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction
Xiaowei Zhu, Yubing Ren, Yanan Cao +3
The rapid advancement of large language models has raised significant concerns regarding their potential misuse by malicious actors. As a result, developing effective detectors to…
A Group Fairness Lens for Large Language Models
Guanqun Bi, Yuqiang Xie, Lei Shen +1
The need to assess LLMs for bias and fairness is critical, with current evaluations often being narrow, missing a broad categorical view. In this paper, we propose evaluating the b…