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Do LLMs Know to Respect Copyright Notice?

Jialiang Xu, Shenglan Li, Zhaozhuo Xu, Denghui Zhang

TL;DR

This study offers a conservative evaluation of the extent to which language models may infringe upon copyrights when processing user input containing protected material and stresses the importance of ensuring LLMs respect copyright regulations when handling user input to prevent unauthorized use or reproduction of protected content.

Abstract

Prior study shows that LLMs sometimes generate content that violates copyright. In this paper, we study another important yet underexplored problem, i.e., will LLMs respect copyright information in user input, and behave accordingly? The research problem is critical, as a negative answer would imply that LLMs will become the primary facilitator and accelerator of copyright infringement behavior. We conducted a series of experiments using a diverse set of language models, user prompts, and copyrighted materials, including books, news articles, API documentation, and movie scripts. Our study offers a conservative evaluation of the extent to which language models may infringe upon copyrights when processing user input containing protected material. This research emphasizes the need for further investigation and the importance of ensuring LLMs respect copyright regulations when handling user input to prevent unauthorized use or reproduction of protected content. We also release a benchmark dataset serving as a test bed for evaluating infringement behaviors by LLMs and stress the need for future alignment.

Do LLMs Know to Respect Copyright Notice?

TL;DR

This study offers a conservative evaluation of the extent to which language models may infringe upon copyrights when processing user input containing protected material and stresses the importance of ensuring LLMs respect copyright regulations when handling user input to prevent unauthorized use or reproduction of protected content.

Abstract

Prior study shows that LLMs sometimes generate content that violates copyright. In this paper, we study another important yet underexplored problem, i.e., will LLMs respect copyright information in user input, and behave accordingly? The research problem is critical, as a negative answer would imply that LLMs will become the primary facilitator and accelerator of copyright infringement behavior. We conducted a series of experiments using a diverse set of language models, user prompts, and copyrighted materials, including books, news articles, API documentation, and movie scripts. Our study offers a conservative evaluation of the extent to which language models may infringe upon copyrights when processing user input containing protected material. This research emphasizes the need for further investigation and the importance of ensuring LLMs respect copyright regulations when handling user input to prevent unauthorized use or reproduction of protected content. We also release a benchmark dataset serving as a test bed for evaluating infringement behaviors by LLMs and stress the need for future alignment.

Paper Structure

This paper contains 47 sections, 1 theorem, 4 equations, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Protecting Intellectual Property Rights in the Era of LLM
  4. Related Work on Copyright vs LLM
  5. Missing Part in Past Evaluation: Respect to the Copyright Notices in Input
  6. Benchmark
  7. Our Advantages
  8. Benchmark Formulation
  9. Quantifying the Effect of Different Query Prompts
  10. Rewriting prompt with LLM: An Importance Sampler.
  11. What Does Copyright Protect?
  12. Books.
  13. News Articles.
  14. Software API Documentation.
  15. Movie Scripts.
  16. ...and 32 more sections

Key Result

Theorem 3.2

Let $\tilde{f}(q,p)$ denote the estimated prompting score defined in Definition def:estimated_prompt_score. We show that Moreover, we have

Figures (6)

  • Figure 1: LLM Responses using Parametric Knowledge vs. Given Context. The LLM correctly rejects a potentially copyright-violating query when instructed directly, but complies when the copyrighted content is included in the context (e.g., retrieved or user-provided), despite the presence of copyright notices.
  • Figure 2: Copyright Notice Example. This is a copyright notice for a book.
  • Figure 3: The Design of Benchmark. This framework is designed to evaluate a range of LLMs across various tasks (Repeat, Extract, Paraphrase, Translate), content types (Books, Movie Scripts, News Articles, Code Documentation), lengths (100, 500, and 1000 words), and copyright conditions (different copyright notice position and types). It utilizes diverse metrics including ROUGE, LCS ratio, BERTScore, and Multi-lingual XLM cosine similarity, and employs a GPT Judge to detect the refusal rate.
  • Figure 4: LLMs' ROUGE Score Against Different Copyright Notice Types. Every color denotes one type of copyright notice. The x-axes of the subplots are binned average ROUGE score each model is getting, and the y-axes represent the frequency of samples in each ROUGE score bin. We found that all LLMs tested were indifferent to different notice types. GPT-4 Turbo is most capable of recognizing copyright notices and complying with them.
  • Figure 5: LLMs' ROUGE Score Distribution Against Different Seed Queries. Each color denotes one unique seed query. The x-axes of the subplots are binned average ROUGE score each model is getting, and the y-axes represent the frequency of samples in each ROUGE score bin. While always resulting in a high extent of copyright violation, the model generation can be sensitive to the seed query. GPT-4 Turbo is more sensitive than other models.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Definition 3.1: Estimated Prompting Score
  • Theorem 3.2: Proprieties of Estimated Prompting Score