Reflection-Bench: probing AI intelligence with reflection

The ability to adapt beliefs or behaviors in response to unexpected outcomes, reflection, is fundamental to intelligent systems' interaction with the world. From a cognitive science perspective, this serves as a core principle of intelligence applicable to both human and AI systems. To address the debate on the intelligence of large language models (LLMs), we propose Reflection-Bench, a comprehensive benchmark comprising 7 tasks spanning Figure 1: Reflection, a fundamental process of intelligence, core cognitive functions crucial for reflection, integrates various cognitive components. To including perception, memory, belief updating, achieve desired outcomes, an intelligent agent must decision-making, prediction, counterfactual predict the external world states and behavioral consequences thinking, and meta-reflection. We evaluate based on prior beliefs. Post-action, discrepancies the performances of 13 prominent LLMs between prediction and observation are perceived, such as OpenAI o1, GPT-4, Claude 3.5 Sonnet, prompting an update of prior belief.