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

arXiv: https://arxiv.org/abs/2606.31250 · HTML: https://mlsanity.com/paper/2606.31250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 78 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [TOFU: A Task of Fictitious Unlearning for LLMs](https://mlsanity.com/paper/2401.06121/) (2024)
- [Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning](https://mlsanity.com/paper/2404.05868/) (2024)
- [Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture](https://mlsanity.com/paper/2306.16092/) (2023)
- [Extracting memorized pieces of (copyrighted) books from open-weight language models](https://mlsanity.com/paper/2505.12546/) (2025)
- [The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models](https://mlsanity.com/paper/2504.07854/) (2025)
- [Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models](https://mlsanity.com/paper/2502.15010/) (2025)
- [The Erasure Illusion: Stress-Testing the Generalization of LLM Forgetting Evaluation](https://mlsanity.com/paper/2512.19025/) (2025)

## Abstract (arXiv)

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, sc`enes `a 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.