Measuring Physical-World Privacy Awareness of Large Language Models: An Evaluation Benchmark
2025-10-06
Summary
The article introduces EAPrivacy, a benchmark designed to evaluate the privacy awareness of Large Language Models (LLMs) when operating in the physical world. The benchmark consists of four tiers, each focusing on different aspects of privacy: identifying sensitive objects, adapting to changing environments, handling task conflicts with inferred privacy, and prioritizing social norms over personal privacy. The results reveal significant deficiencies in current LLMs, with models often failing to prioritize privacy adequately, especially in dynamic and ethically complex scenarios.
Why This Matters
As LLMs are increasingly employed in embodied agents that interact with the physical world, understanding their ability to handle privacy is crucial. These agents are expected to operate in sensitive environments like homes and hospitals, where maintaining privacy is a fundamental requirement. The findings highlight a gap in current AI models' capabilities, underscoring the need for improved privacy-aligned AI systems that can responsibly operate in real-world settings.
How You Can Use This Info
Professionals working with AI technologies should consider the limitations of current LLMs in handling privacy, especially in physical contexts. This awareness can inform the development and deployment strategies of AI systems, ensuring they are equipped with better privacy-preserving capabilities. Additionally, those in policy-making or regulatory roles can use these insights to push for standards and guidelines that ensure AI systems respect privacy in their operations.