Mind vs. Mouth: On Measuring Re-judge Inconsistency of Social Bias in Large Language Models

TL;DR

This paper investigates the phenomenon of re-judge inconsistency in large language models, revealing parallels with human implicit and explicit social biases, and offers insights into their cognitive-like behaviors.

Contribution

It introduces a two-stage approach to measure social bias in LLMs and uncovers a stable re-judge inconsistency phenomenon analogous to human cognitive bias.

Findings

Re-judge inconsistency is highly stable across models.

LLMs exhibit parallel behaviors to human implicit and explicit biases.

Psychological theories can enhance understanding of LLM social biases.

Abstract

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.