ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cues

TL;DR

ImplicitBBQ introduces a new benchmark for evaluating implicit bias in large language models using characteristic cues across multiple social dimensions, revealing persistent biases despite current mitigation strategies.

Contribution

The paper presents ImplicitBBQ, a novel benchmark that assesses implicit bias through culturally associated cues, addressing limitations of name-based proxies and covering diverse social categories.

Findings

Implicit bias in ambiguous contexts is over six times higher than explicit bias.

Prompting strategies reduce implicit bias by up to 84%, but caste bias remains significantly high.

Current alignment methods do not fully mitigate culturally grounded stereotypes.

Abstract

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.