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arXiv:2606. 31250v1 Announce Type: cross 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.
arXiv:2610.17002v1 Announce Type: cross Abstract: Large language models are trained on collections that may include scholarly works, while authors generally lack work-level information about whether...
arXiv:2601. 15828v4 Announce Type: replace-cross Abstract: This study investigates whether professional translators without prior specialized training can reliably identify short stories generated in Italian by artificial intelligence (AI).
The paper demonstrates that fine‑tuning large language models on a single author’s works can trigger the models to reproduce large verbatim excerpts from copyrighted books, even when prompted only with semantic descriptions. Experiments on GPT‑4o, Gemini‑2.5‑Pro, and DeepSeek‑V3.1 show up to 85‑90% recall of held‑out books, with spans exceeding 460 words, and this effect generalizes across authors and model providers. The findings suggest that fine‑tuning reactivates latent memorization from pre‑training, revealing a widespread vulnerability in industry models.
The paper argues that modern inference pipelines add an unseen layer of control between a language model’s frozen weights and its output, altering probability distributions before token selection. It introduces the concepts of the Inference Attribution Problem, Probability Placement, and Inference Policy Transparency to describe how such interventions can bias generated language toward specific frames and how these biases cannot be traced solely to model weights. The authors discuss the governance, security, and economic implications of these undisclosed inference policies, referencing EU AI Act, Digital Services Act, and FTC doctrines.
arXiv:2606.26040v2 Announce Type: replace Abstract: AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers expe...