情境完整性生成模型：研究 LLM 对话中信息分享是否得当
A Generative Model of Contextual Integrity: Appropriate vs. Inappropriate Information Sharing
AI 导读
NeurIPS 2026 主会收录的论文提出一个情境完整性的生成模型，用两个由大语言模型扮演的模拟对话角色研究信息分享：一方持有隐私信息，另一方设法获取。实验显示，角色会在需要时向有权接收者分享信息，但也会向无权者泄露。该模型可用于研究信息披露与拒绝如何随情境、决策逻辑、角色性格和底层模型变化而在对话中演进。
正文 · 原文
Abstract: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.
| Comments: | Accepted as a main conference paper at NeurIPS 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.08284 [cs.AI] |
| (or arXiv:2611.08284v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08284 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Joel Leibo [view email]
[v1]
Thu, 5 Nov 2026 16:48:31 UTC (89 KB)
来源：arXiv cs.AI · arxiv.org