2024/04/04 by Farnaz Kohankhaki, Kohankhaki, Farnaz, Jacob-Junqi Tian +6
Computer Science · Psychology · #Cognitive psychology #Computer science #Econometrics #Economics #Epistemology #Mathematical economics #Natural Language Processing Techniques #Norm (philosophy) #Philosophy #Psychology
paper · pdf · doi:10.48550/arxiv.2404.03471
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/04/04 · openalex created_date 2024/04/06 · openalex updated_date 2026/08/06
Bias in large language models (LLMs) has many forms, from overt discrimination to implicit stereotypes. Counterfactual bias evaluation is a widely used approach to quantifying bias and often relies on template-based probes that explicitly state group membership. It aims to measure whether the outcome of a task performed by an LLM is invariant to a change in group membership. In this work, we find that template-based probes can introduce systematic distortions in bias measurements. Specifically, we consistently find that such probes suggest that LLMs classify text associated with White race as negative at disproportionately elevated rates. This is observed consistently across a large collection of LLMs, over several diverse template-based probes, and with different classification approaches. We hypothesize that this arises artificially due to linguistic asymmetries present in LLM pretraining data, in the form of markedness, (e.g., Black president vs. president) and templates used for bias measurement (e.g., Black president vs. White president). These findings highlight the need for more rigorous methodologies in counterfactual bias evaluation, ensuring that observed disparities reflect genuine biases rather than artifacts of linguistic conventions.