Reinforcing privacy reasoning in LLMs via normative simulacra from fiction
Information handling practices of LLM agents are broadly misaligned with the contextual privacy expectations of their users. Contextual Integrity (CI) provides a principled framework, defining privacy as the appropriate flow of information within context-relative norms. However, existing approaches either double inference cost via supervisor-assistant architectures, or fine-tune on narrow task-specific data. We propose extracting normative simulacra (structured representations of norms and information flows) from fiction novels and using them to fine-tune LLMs via supervised learning followed by GRPO reinforcement learning. Our composite reward function combines programmatic signals, incl...
| Authors | Matt Franchi, Madiha Zahrah Choksi, Harold Triedman et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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