We Should Separate Memorization from Copyright

2026-02-09

AI Summary

This study addresses the frequent conflation of “model memorization” and “substantial copying” in current copyright assessments of large language models, which has led to a disconnect between technical analyses and legal standards. It proposes, for the first time, a risk assessment framework grounded in copyright law principles that focuses specifically on model outputs rather than internal mechanisms. Through an interdisciplinary analysis bridging legal doctrine and technical practice, the work critically examines the limitations of conventional reconstruction-based evaluation methods within a copyright context. The resulting framework offers a legally coherent foundation for model auditing, policymaking, and future research, thereby advancing the normative governance of AI-generated content under copyright law.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The widespread use of foundation models has introduced a new risk factor of copyright issue. This issue is leading to an active, lively and on-going debate amongst the data-science community as well as amongst legal scholars. Where claims and results across both sides are often interpreted in different ways and leading to different implications. Our position is that much of the technical literature relies on traditional reconstruction techniques that are not designed for copyright analysis. As a result, memorization and copying have been conflated across both technical and legal communities and in multiple contexts. We argue that memorization, as commonly studied in data science, should not be equated with copying and should not be used as a proxy for copyright infringement. We distinguish technical signals that meaningfully indicate infringement risk from those that instead reflect lawful generalization or high-frequency content. Based on this analysis, we advocate for an output-level, risk-based evaluation process that aligns technical assessments with established copyright standards and provides a more principled foundation for research, auditing, and policy.

Problem

• memorization
• copyright infringement
• copying
• foundation models
• risk assessment

Innovation

• memorization
• copyright infringement
• foundation models
• risk-based evaluation
• output-level analysis