Exploring Memorization and Copyright Violation in Frontier LLMs: A Study of the New York Times v. OpenAI 2023 Lawsuit (2412.06370v1)

Abstract: Copyright infringement in frontier LLMs has received much attention recently due to the New York Times v. OpenAI lawsuit, filed in December 2023. The New York Times claims that GPT-4 has infringed its copyrights by reproducing articles for use in LLM training and by memorizing the inputs, thereby publicly displaying them in LLM outputs. Our work aims to measure the propensity of OpenAI's LLMs to exhibit verbatim memorization in its outputs relative to other LLMs, specifically focusing on news articles. We discover that both GPT and Claude models use refusal training and output filters to prevent verbatim output of the memorized articles. We apply a basic prompt template to bypass the refusal training and show that OpenAI models are currently less prone to memorization elicitation than models from Meta, Mistral, and Anthropic. We find that as models increase in size, especially beyond 100 billion parameters, they demonstrate significantly greater capacity for memorization. Our findings have practical implications for training: more attention must be placed on preventing verbatim memorization in very large models. Our findings also have legal significance: in assessing the relative memorization capacity of OpenAI's LLMs, we probe the strength of The New York Times's copyright infringement claims and OpenAI's legal defenses, while underscoring issues at the intersection of generative AI, law, and policy.

Examine Memorization and Legal Concerns in LLMs

The paper "Exploring Memorization and Copyright Violation in Frontier LLMs: A Study of the New York Times v. OpenAI 2023 Lawsuit" addresses the complex issue of verbatim memorization in LLMs like OpenAI's GPT-4, particularly in the context of ongoing legal battles concerning copyright infringement. This investigation is motivated by concerns around verbatim reproduction of copyrighted materials, exemplified by a lawsuit from The New York Times against OpenAI. The authors meticulously examine the proclivity of various LLMs to retain and output verbatim snippets of training data, scrutinizing their potential breach of copyright laws.

The central objective of this paper is to quantify the capacity for memorization in OpenAI’s models as compared to other state-of-the-art LLMs, such as those from Meta, Mistral, and Anthropic. The authors offer an insightful analysis of LLM memorization, evidenced by methods to bypass restrictions that prevent verbatim outputs. The research demonstrates that although powerhouse models like GPT-4 and Claude employ refusal training and output filters to mitigate this issue, they do not completely eliminate memorization, particularly as models approach or exceed 100 billion parameters.

Key Findings and Results

1. Model Size and Memorization: The authors present compelling evidence suggesting that the capacity for memorization substantially increases with model size. For instance, models with greater than 100 billion parameters are significantly more prone to embedding verbatim excerpts of their training data into generated outputs.
2. Comparative Analysis of LLMs: When comparing the extent of memorization across various models, OpenAI's LLMs appear to exhibit less verbatim replication than their peers, such as those from Meta and Mistral. This could potentially weaken The New York Times' legal claims against OpenAI, as their models are not necessarily the worst offenders in terms of memorization capabilities.
3. Effectiveness of Mitigation Techniques: The paper details defensive measures implemented by model developers, such as output filters that impede the regurgitation of copyrighted content. This shows promise as a viable approach to mitigating legal risks associated with intellectual property infringement.

Implications and Future Directions

The implications of this research extend into both practical and legal realms. Practically, training processes for LLMs need augmentation to better address the challenges of verbatim memorization without compromising performance—especially as model architecture continues to scale. Theoretically, this brings forth considerations on the legality of memorization, dovetailing with concepts such as fair use and contributory infringement in copyright law. These insights could dynamically influence the interpretation and application of intellectual property rights in the context of AI.

The authors aptly suggest that future work should focus on refining techniques to prevent the unauthorized replication of copyrighted content while preserving the semantic efficacy of generated outputs. Research could explore advanced methods like differential privacy or machine unlearning, possibly offering better alignment between LLM utility and legal compliance. Moreover, validating these findings with experiments on different datasets could enhance the robustness of such conclusions and provide further evidence for shaping AI policies and standards.

In conclusion, this comprehensive paper of LLM memorization critically informs both technological practice and legal discourse surrounding AI systems. The exploration offers valuable insights into how the industry might navigate the emerging landscape of AI deployment amid evolving legal frameworks and societal expectations.