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The paper introduces ImplicitBBQ, a QA benchmark designed to evaluate implicit bias in LLMs using characteristic-based cues (culturally associated attributes) across demographics like age, gender, region, religion, caste, and socioeconomic status. Experiments on 11 models reveal that implicit bias is significantly higher than explicit bias, particularly in open-weight models, and that standard mitigation techniques like safety prompting and chain-of-thought reasoning are ineffective. Even few-shot prompting, while reducing implicit bias overall, leaves caste bias disproportionately high, suggesting a failure to address deeply ingrained stereotypes.
LLMs can be six times more biased when demographic identity is conveyed implicitly through cultural cues, even when explicit biases are suppressed.
Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit biases when identity is conveyed indirectly. Existing benchmarks use name based proxies to detect implicit biases, which carry weak associations with many social demographics and cannot extend to dimensions like age or socioeconomic status. We introduce ImplicitBBQ, a QA benchmark that evaluates implicit bias through characteristic based cues, culturally associated attributes that signal implicitly, across age, gender, region, religion, caste, and socioeconomic status. Evaluating 11 models, we find that implicit bias in ambiguous contexts is over six times higher than explicit bias in open weight models. Safety prompting and chain-of-thought reasoning fail to substantially close this gap; even few-shot prompting, which reduces implicit bias by 84%, leaves caste bias at four times the level of any other dimension. These findings indicate that current alignment and prompting strategies address the surface of bias evaluation while leaving culturally grounded stereotypic associations largely unresolved. We publicly release our code and dataset for model providers and researchers to benchmark potential mitigation techniques.
