Contextual Integrity in Large Language Models: A Review

The rapid advancements in large language models (LLMs) have transformed natural language processing, enabling their application in diverse domains such as conversational agents and decision-support systems in sensitive areas like healthcare, finance, and eldercare. However, as LLMs are increasingly integrated into real-world contexts, concerns about their adherence to ethical principles, privacy norms, and contextual expectations have become critical. Privacy preservation is particularly pressing in interactions involving personal or sensitive data, where ensuring that LLMs align with societal norms while mitigating risks of information leakage is essential to fostering trust and ensuring responsible deployment. Contextual integrity (CI) provides a robust framework to address these challenges, emphasizing that information flows should adhere to context-specific social norms. This principle is especially vital in sensitive applications, where LLMs must evaluate roles, information attributes, and transmission principles to maintain ethical behavior. Despite their linguistic proficiency, LLMs often fail to recognize and adapt to nuanced contextual norms, a limitation exacerbated by their probabilistic nature and the biases in their training data, which can lead to inappropriate or harmful outputs. Addressing these shortcomings requires rigorous evaluation methodologies and fine-tuning strategies that embed societal and contextual norms into the models.