Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law

• URL: http://arxiv.org/abs/2606.31250v1
• Date: Tue, 30 Jun 2026 07:25:32 GMT
• Title: Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law
• Abstract summary: Large language models (LLM) trained on web-scale corpora generate output that may infringe copyright.<n>EU copyright doctrine applies a broader standards: substantial similarity, which extends to stylistic choices, narrative structure, and creative elaboration.<n>We introduce PSALM, an LLM-as-a-judge framework that operationalises EU copyright doctrine through ten evaluators.
• Score: 3.9292613467235262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLM) trained on web-scale corpora generate output that may infringe copyright, yet existing technical safeguards focus narrowly on verbatim memorisation. EU copyright doctrine applies a broader standards: substantial similarity, which extends to stylistic choices, narrative structure, and creative elaboration. This mismatch between what current methods detect and what the law protects leaves a significant compliance gap. We introduce PSALM, an LLM-as-a-judge framework that operationalises EU copyright doctrine through ten evaluators assessing computational overlap, stylistic dimensions (writing style, narrative voice), content dimensions (character, plot, scene, world building), and statutory exceptions (parody, pastiche, quotation, scènes à faire). Applying PSALM to Llama~3.2 models fine-tuned on translated historical Dutch literary works, we find that: 1) instruction-tuned models exhibit non-trivial baseline stylistic similarity prior to corpus exposure; 2) fine-tuning induces systematic stylistic appropriation across all infringement-relevant dimensions, extending beyond verbatim memorisation to abstract narrative patterns; 3) Negative Preference Optimisation unlearning substantially reduces similarity but leaves detectable residual stylistic patterns. These findings indicate that safeguards targeting literal copying alone are insufficient to mitigate broader copyright risks. PSALM provides infrastructure for auditable, legally informed compliance evaluation, though the relationship between automated similarity scores and infringement determinations requires validation by legal experts. This work bridges qualitative legal standards and quantitative technical measurement, exposing fundamental tensions between generative AI and EU intellectual property law.

Related papers

• SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking [58.475471437150674]
  We propose sequential watermarking for soft prompts (SWAP)<n>SWAP encodes watermarks through a specific order of defender-specified out-of-distribution classes.<n>Experiments on 11 datasets demonstrate SWAP's effectiveness, harmlessness, and robustness against potential adaptive attacks.
  arXiv  Detail & Related papers  (2025-11-05T13:48:48Z)
• SoK: Large Language Model Copyright Auditing via Fingerprinting [69.14570598973195]
  We introduce a unified framework and formal taxonomy that categorizes existing methods into white-box and black-box approaches.<n>We propose LeaFBench, the first systematic benchmark for evaluating LLM fingerprinting under realistic deployment scenarios.
  arXiv  Detail & Related papers  (2025-08-27T12:56:57Z)
• Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair Use [44.99833362998488]
  This paper presents a domain-specific implementation of Retrieval-Augmented Generation tailored to the Fair Use Doctrine in U.S. copyright law.<n>Motivated by the increasing prevalence of DMCA takedowns and the lack of accessible legal support for content creators, we propose a structured approach that combines semantic search with legal knowledge graphs and court citation networks to improve retrieval quality and reasoning reliability.
  arXiv  Detail & Related papers  (2025-05-04T15:53:49Z)
• CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models [58.58208005178676]
  We propose CopyJudge, a novel automated infringement identification framework.<n>We employ an abstraction-filtration-comparison test framework to assess the likelihood of infringement.<n>We introduce a general LVLM-based mitigation strategy that automatically optimize infringing prompts.
  arXiv  Detail & Related papers  (2025-02-21T08:09:07Z)
• Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning [0.0]
  Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose risks by learning and generating copyrighted material.<n>We propose Stable Sequential Unlearning (SSU), a novel framework designed to unlearn copyrighted content from LLMs over multiple time steps.<n>SSU sometimes achieves an effective trade-off between unlearning efficacy and general-purpose language abilities, outperforming existing baselines, but it's not a cure-all for unlearning copyrighted material.
  arXiv  Detail & Related papers  (2024-12-16T20:01:06Z)
• Evaluating Copyright Takedown Methods for Language Models [100.38129820325497]
  Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material. This paper introduces the first evaluation of the feasibility and side effects of copyright takedowns for LMs. We examine several strategies, including adding system prompts, decoding-time filtering interventions, and unlearning approaches.
  arXiv  Detail & Related papers  (2024-06-26T18:09:46Z)
• Avoiding Copyright Infringement via Large Language Model Unlearning [24.050754626661124]
  We propose a novel framework designed to unlearn copyrighted content from Large Language Models over multiple time steps.<n>We improve unlearning efficacy by introducing random labeling loss and ensuring the model retains its general-purpose knowledge.<n> Experimental results show that SSU achieves an effective trade-off between unlearning efficacy and general-purpose language abilities.
  arXiv  Detail & Related papers  (2024-06-16T14:12:37Z)
• 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)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models [52.49582606341111]
  Copyright law confers creators the exclusive rights to reproduce, distribute, and monetize their creative works. Recent progress in text-to-image generation has introduced formidable challenges to copyright enforcement. We introduce a novel pipeline that harmonizes CLIP, ChatGPT, and diffusion models to curate a dataset.
  arXiv  Detail & Related papers  (2024-01-04T11:14:01Z)

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.