CASE-Bench introduces a new approach to evaluating LLM safety by considering the context in which potentially problematic queries appear, avoiding unnecessary refusals that diminish user experience.

• Addresses the limitation of current safety benchmarks that focus only on refusing individual problematic queries
• Evaluates LLM responses within various contextual scenarios rather than in isolation
• Provides a more nuanced safety assessment that balances protection with usability
• Supports better alignment with human values for safer LLM deployment

This research advances security practices by recognizing that context matters in safety evaluations, potentially leading to more practical, user-friendly AI safety mechanisms that don't compromise on protection.

CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models