Do LLMs Know to Respect Copyright Notice?

• URL: http://arxiv.org/abs/2411.01136v1
• Date: Sat, 02 Nov 2024 04:45:21 GMT
• Title: Do LLMs Know to Respect Copyright Notice?
• Authors: Jialiang Xu, Shenglan Li, Zhaozhuo Xu, Denghui Zhang,
• Abstract summary: We investigate whether language models infringe upon copyrights when processing user input containing protected material. Our study offers a conservative evaluation of the extent to which language models may infringe upon copyrights. This research emphasizes the need for further investigation and the importance of ensuring LLMs respect copyright regulations.
• Score: 11.14140288980773
• License:
• 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.

Related papers

• Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models [47.694137341509304]
  We evaluate the attribution sensitivity and bias with respect to authorship information in large language models. Our results show that adding authorship information to source documents can significantly change the attribution quality of LLMs by 3% to 18%. Our findings indicate that metadata of source documents can influence LLMs' trust, and how they attribute their answers.
  arXiv  Detail & Related papers  (2024-10-16T08:55:49Z)
• Measuring Copyright Risks of Large Language Model via Partial Information Probing [14.067687792633372]
  We explore the data sources used to train Large Language Models (LLMs) We input a portion of a copyrighted text into LLMs, prompt them to complete it, and then analyze the overlap between the generated content and the original copyrighted material. Our findings demonstrate that LLMs can indeed generate content highly overlapping with copyrighted materials based on these partial inputs.
  arXiv  Detail & Related papers  (2024-09-20T18:16:05Z)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? [62.72729485995075]
  We investigate the effectiveness of watermarking as a deterrent against the generation of copyrighted texts. We find that watermarking adversely affects the success rate of Membership Inference Attacks (MIAs) We propose an adaptive technique to improve the success rate of a recent MIA under watermarking.
  arXiv  Detail & Related papers  (2024-07-24T16:53:09Z)
• SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation [24.644101178288476]
  Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns. LLMs may infringe on copyrights or overly restrict non-copyrighted texts. We propose lightweight, real-time defense to prevent the generation of copyrighted text.
  arXiv  Detail & Related papers  (2024-06-18T18:00:03Z)
• LLMs and Memorization: On Quality and Specificity of Copyright Compliance [0.0]
  Memorization in large language models (LLMs) is a growing concern. LLMs have been shown to easily reproduce parts of their training data, including copyrighted work. This is an important problem to solve, as it may violate existing copyright laws as well as the European AI Act.
  arXiv  Detail & Related papers  (2024-05-28T18:01:52Z)
• Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models [63.91178922306669]
  We introduce Silent Guardian, a text protection mechanism against large language models (LLMs) By carefully modifying the text to be protected, TPE can induce LLMs to first sample the end token, thus directly terminating the interaction. We show that SG can effectively protect the target text under various configurations and achieve almost 100% protection success rate in some cases.
  arXiv  Detail & Related papers  (2023-12-15T10:30:36Z)
• LLatrieval: LLM-Verified Retrieval for Verifiable Generation [67.93134176912477]
  Verifiable generation aims to let the large language model (LLM) generate text with supporting documents. We propose LLatrieval (Large Language Model Verified Retrieval), where the LLM updates the retrieval result until it verifies that the retrieved documents can sufficiently support answering the question. Experiments show that LLatrieval significantly outperforms extensive baselines and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-14T01:38:02Z)
• Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark [58.60940048748815]
  Companies have begun to offer Embedding as a Service (E) based on large language models (LLMs) E is vulnerable to model extraction attacks, which can cause significant losses for the owners of LLMs. We propose an Embedding Watermark method called EmbMarker that implants backdoors on embeddings.
  arXiv  Detail & Related papers  (2023-05-17T08:28:54Z)
• Whose Text Is It Anyway? Exploring BigCode, Intellectual Property, and Ethics [1.933681537640272]
  This position paper probes the copyright interests of open data sets used to train large language models (LLMs) Our paper asks, how do LLMs trained on open data sets circumvent the copyright interests of the used data?
  arXiv  Detail & Related papers  (2023-04-06T03:09:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.