Covert Bias: The Severity of Social Views' Unalignment in Language Models Towards Implicit and Explicit Opinion
This addresses bias identification in LLMs for socially nuanced topics, but it is incremental as it builds on existing bias evaluation methods.
The study examined how implicit versus explicit language affects bias amplification in large language models, finding that LLMs show a discrepancy in identifying implicit versus explicit opinions with a general bias toward explicit opinions of opposing stances, and bias-aligned models generate more cautious responses using uncertainty phrases.
While various approaches have recently been studied for bias identification, little is known about how implicit language that does not explicitly convey a viewpoint affects bias amplification in large language models. To examine the severity of bias toward a view, we evaluated the performance of two downstream tasks where the implicit and explicit knowledge of social groups were used. First, we present a stress test evaluation by using a biased model in edge cases of excessive bias scenarios. Then, we evaluate how LLMs calibrate linguistically in response to both implicit and explicit opinions when they are aligned with conflicting viewpoints. Our findings reveal a discrepancy in LLM performance in identifying implicit and explicit opinions, with a general tendency of bias toward explicit opinions of opposing stances. Moreover, the bias-aligned models generate more cautious responses using uncertainty phrases compared to the unaligned (zero-shot) base models. The direct, incautious responses of the unaligned models suggest a need for further refinement of decisiveness by incorporating uncertainty markers to enhance their reliability, especially on socially nuanced topics with high subjectivity.