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arXiv:2611.08284 · v1

A Generative Model of Contextual Integrity: Appropriate vs. Inappropriate Information Sharing

首次提交 2026-11-06 00:48
版本更新 2026-11-06 00:48
雷达观测 2026-11-06 12:09
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摘要

Contextual integrity (CI) holds that appropriate information flow depends on context-specific norms. The same disclosure may be appropriate in one context and inappropriate in another. We build a generative model of contextual integrity to study information sharing through simulated conversations. Two actors played by large language models exchange messages, with one holding private information and the other seeking it. We examine how sharing and refusal vary with the situation, the holder's decision logic, disposition, and the underlying language model. Actors shared information with the rightful recipients when it was needed, but also disclosed it to people who were not entitled to receive it. The model provides a way to study how disclosure and refusal develop through conversation as the situations and the actors vary.

作者备注:Accepted as a main conference paper at NeurIPS 2026

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基于摘要 · 非全文审阅

作者构建了一个基于大语言模型对话模拟的情境完整性生成模型,用于研究信息分享与拒绝如何随情境和行动者属性变化。

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v1 · 2026-11-06 00:48 · 最新观测版本

A Generative Model of Contextual Integrity: Appropriate vs. Inappropriate Information Sharing

Contextual integrity (CI) holds that appropriate information flow depends on context-specific norms. The same disclosure may be appropriate in one context and inappropriate in another. We build a generative model of contextual integrity to study information sharing through simulated conversations. Two actors played by large language models exchange messages, with one holding private information and the other seeking it. We examine how sharing and refusal vary with the situation, the holder's decision logic, disposition, and the underlying language model. Actors shared information with the rightful recipients when it was needed, but also disclosed it to people who were not entitled to receive it. The model provides a way to study how disclosure and refusal develop through conversation as the situations and the actors vary.

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