The rapid scaling of large language models (LLMs) has raised concerns about the
transparency and fair use of the pretraining data used for training them. Detecting such …

Large Language Models (LLMs) have demonstrated impressive capabilities in generating
diverse and contextually rich text. However, concerns regarding copyright infringement arise …

We consider the problem of a training data proof, where a data creator or owner wants to
demonstrate to a third party that some machine learning model was trained on their data …

As the scale of training corpora for large language models (LLMs) grows, model developers
become increasingly reluctant to disclose details on their data. This lack of transparency …

Generative models such as Large Language Models (LLM) and Multimodal Large
Language models (MLLMs) trained on massive web corpora can memorize and disclose …

The training data in large language models is key to their success, but it also presents
privacy and security risks, as it may contain sensitive information. Detecting pre-training data …

The widespread deployment of large language models (LLMs) has led to impressive
advancements, yet information about their training data, a critical factor in their performance …

The rapid development of Large Language Models (LLMs) like GPT-4, Claude-3, and
Gemini has transformed the field of natural language processing. However, it has also …

The immense datasets used to develop Large Language Models (LLMs) often include
copyright-protected content, typically without the content creator's consent. Copyright traps …

Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but
also pose risks by learning and generating copyrighted material, leading to significant legal …