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

Understand

Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption.

• Current LLM safety benchmarks often focus solely on the refusal of individual problematic queries, which overlooks the importance of the context where the query occurs and may cause undesired refusal of queries under safe contexts that diminish user experience.
• Addressing this gap, we introduce CASE-Bench, a Context-Aware SafEty Benchmark that integrates context into safety assessments of LLMs.
• CASE-Bench assigns distinct, formally described contexts to categorized queries based on Contextual Integrity theory.