Survey on ai-generated plagiarism detection: The impact of large language models on academic integrity

S Pudasaini, L Miralles-Pechuán, D Lillis… - Journal of Academic …, 2024‏ - Springer
A survey conducted in 2023 surveyed 3,017 high school and college students. It found that
almost one-third of them confessed to using ChatGPT for assistance with their homework …

Adaptive text watermark for large language models

Y Liu, Y Bu - arxiv preprint arxiv:2401.13927, 2024‏ - arxiv.org
The advancement of Large Language Models (LLMs) has led to increasing concerns about
the misuse of AI-generated text, and watermarking for LLM-generated text has emerged as a …

Survey on plagiarism detection in large language models: The impact of chatgpt and gemini on academic integrity

S Pudasaini, L Miralles-Pechuán, D Lillis… - arxiv preprint arxiv …, 2024‏ - arxiv.org
The rise of Large Language Models (LLMs) such as ChatGPT and Gemini has posed new
challenges for the academic community. With the help of these models, students can easily …

Watermarking techniques for large language models: A survey

Y Liang, J **ao, W Gan, PS Yu - arxiv preprint arxiv:2409.00089, 2024‏ - arxiv.org
With the rapid advancement and extensive application of artificial intelligence technology,
large language models (LLMs) are extensively used to enhance production, creativity …

Watermarking Large Language Models and the Generated Content: Opportunities and Challenges

R Zhang, F Koushanfar - arxiv preprint arxiv:2410.19096, 2024‏ - arxiv.org
The widely adopted and powerful generative large language models (LLMs) have raised
concerns about intellectual property rights violations and the spread of machine-generated …

Watermarking language models with error correcting codes

P Chao, Y Sun, E Dobriban, H Hassani - arxiv preprint arxiv:2406.10281, 2024‏ - arxiv.org
Recent progress in large language models enables the creation of realistic machine-
generated content. Watermarking is a promising approach to distinguish machine-generated …

SoK: Watermarking for AI-Generated Content

X Zhao, S Gunn, M Christ, J Fairoze, A Fabrega… - arxiv preprint arxiv …, 2024‏ - arxiv.org
As the outputs of generative AI (GenAI) techniques improve in quality, it becomes
increasingly challenging to distinguish them from human-created content. Watermarking …

De-mark: Watermark Removal in Large Language Models

R Chen, Y Wu, J Guo, H Huang - arxiv preprint arxiv:2410.13808, 2024‏ - arxiv.org
Watermarking techniques offer a promising way to identify machine-generated content via
embedding covert information into the contents generated from language models (LMs) …

Intellectual Property Protection for Deep Learning Model and Dataset Intelligence

Y Jiang, Y Gao, C Zhou, H Hu, A Fu… - arxiv preprint arxiv …, 2024‏ - arxiv.org
With the growing applications of Deep Learning (DL), especially recent spectacular
achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the …

A Watermark for Order-Agnostic Language Models

R Chen, Y Wu, Y Chen, C Liu, J Guo… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Statistical watermarking techniques are well-established for sequentially decoded language
models (LMs). However, these techniques cannot be directly applied to order-agnostic LMs …