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

TL;DR

This paper introduces DIF, a new benchmark for measuring implicit bias in Large Language Models, revealing biases and their relationship with accuracy through a novel evaluation method.

Contribution

The paper presents a standardized, interpretable benchmark for assessing implicit bias in LLMs, addressing a critical gap in bias measurement methods.

Findings

DIF effectively detects implicit bias in LLMs.

An inverse relationship between bias and accuracy was observed.

The method includes a statistical robustness check.

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, which is combined with a statistical robustness check using a null model. We demonstrate that this method can validate the presence of implicit bias in LLM behavior and find an novel inverse trend between question answering accuracy and implicit bias, supporting our argument.