Mind vs. Mouth: On Measuring Re-judge Inconsistency of Social Bias in
  Large Language Models
Yachao Zhao Bo Wang Dongming Zhao Kun Huang Yan Wang Ruifang He Yuexian Hou
Recent researches indicate that Pre-trained Large Language Models (LLMs)
possess cognitive constructs similar to those observed in humans, prompting
researchers to investigate the cognitive aspects of LLMs. This paper focuses on
explicit and implicit social bias, a distinctive two-level cognitive construct
in psychology. It posits that individuals' explicit social bias, which is their
conscious expression of bias in the statements, may differ from their implicit
social bias, which represents their unconscious bias. We propose a two-stage
approach and discover a parallel phenomenon in LLMs known as "re-judge
inconsistency" in social bias. In the initial stage, the LLM is tasked with
automatically completing statements, potentially incorporating implicit social
bias. However, in the subsequent stage, the same LLM re-judges the biased
statement generated by itself but contradicts it. We propose that this re-judge
inconsistency can be similar to the inconsistency between human's unaware
implicit social bias and their aware explicit social bias. Experimental
investigations on ChatGPT and GPT-4 concerning common gender biases examined in
psychology corroborate the highly stable nature of the re-judge inconsistency.
This finding may suggest that diverse cognitive constructs emerge as LLMs'
capabilities strengthen. Consequently, leveraging psychological theories can
provide enhanced insights into the underlying mechanisms governing the
expressions of explicit and implicit constructs in LLMs.