  Dual process theory posits that human cognition arises via two systems.
System 1, which is a quick, emotional, and intuitive process, which is subject
to cognitive biases, and System 2, a slow, onerous, and deliberate process. NLP
researchers often compare zero-shot prompting in LLMs to System 1 reasoning and
chain-of-thought (CoT) prompting to System 2. In line with this interpretation,
prior research has found that using CoT prompting in LLMs leads to reduced
gender bias. We investigate the relationship between bias, CoT prompting, and
dual process theory in LLMs directly. We compare zero-shot, CoT, and a variety
of dual process theory-based prompting strategies on two bias datasets spanning
nine different social bias categories. We also use human and machine personas
to determine whether the effects of dual process theory in LLMs are based on
modeling human cognition or inherent to the system. We find that a human
persona, System 2, and CoT prompting all tend to reduce social biases in LLMs,
though the best combination of features depends on the exact model and bias
category -- resulting in up to a 13 percent drop in stereotypical judgments by
an LLM.
