Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law
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, styli
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownDataset of permissively licensed code released →
- PossiblePossibly related (embedding) · 46%Improving machine-translated novels via style transfer — looking for advice on the faithfulness/fluency tradeoff [P] →
- PossiblePossibly related (embedding) · 48%p-e-w/heretic →
- PossiblePossibly related (embedding) · 47%Anthropic got sued for using copyrighted books for LLM training →
- PossiblePossibly related (embedding) · 54%OUTPUT v. INPUT- Copyright Ownership Challenges in the Era of Artificial Intelligence - The National Law Review →
