Capability Evaluation

Capability evaluation in AI focuses on accurately measuring the abilities of artificial intelligence systems, particularly large language models (LLMs), across diverse tasks. Current research emphasizes developing robust and reliable evaluation methods, including those that require minimal human supervision and address challenges like strategic underperformance ("sandbagging") by AI systems. These efforts are crucial for ensuring the safe and responsible deployment of AI, informing model development, and providing a more nuanced understanding of AI capabilities beyond simple benchmark scores. The development of new benchmarks and evaluation frameworks, often incorporating multi-turn interactions and dynamic assessments, is a key focus.