{
  "id": 415,
  "url": "https://arxiv.org/abs/2606.31250v1",
  "title": "Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law",
  "summary": "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",
  "authors": "Noah Scharrenberg, Chang Sun",
  "category": "research",
  "topics": "regulation,copyright-ip",
  "orgs": null,
  "regions": "eu",
  "published_at": "2026-06-30T07:25:32.000Z",
  "fetched_at": "2026-07-14T14:14:32.645Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/415",
  "original_url": "https://arxiv.org/abs/2606.31250v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}