DIF: A Framework for Benchmarking and Verifying Implicit Bias in LLMs

ACL ARR 2025 May Submission1223 Authors

Abstract: As Large Language Models (LLMs) have risen in prominence over the past few years, there has been concern over the potential biases in LLMs inherited from the training data. Previous studies have examined how LLMs exhibit implicit bias, such as when response generation changes when different social contexts are introduced. We argue that this implicit bias is not only an ethical, but also a technical issue, as it reveals an inability of LLMs to accommodate extraneous information. However, unlike other measures of LLM intelligence, there are no standard methods to benchmark this specific subset of LLM bias. To bridge this gap, we developed a method for calculating an easily interpretable benchmark, DIF (Demographic Implicit Fairness), by evaluating preexisting LLM logic and math problem datasets with sociodemographic personas. We demonstrate that this method can statistically validate the presence of implicit bias in LLM behavior and find an inverse trend between question answering accuracy and implicit bias, supporting our argument.

Paper Type: Short

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: model bias/fairness evaluation

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 1223