Unmasking the Mask -- Evaluating Social Biases in Masked Language Models

TL;DR

This paper critiques existing social bias evaluation metrics for Masked Language Models (MLMs), introduces a new measure called All Unmasked Likelihood (AUL), and demonstrates its effectiveness in accurately detecting biases.

Contribution

The authors propose AUL, a novel bias evaluation metric for MLMs that overcomes limitations of previous methods by predicting all tokens from unmasked input, improving bias detection accuracy.

Findings

AUL effectively detects various biases in MLMs.

Existing metrics tend to overestimate biases and are influenced by unmasked tokens.

AUL with attention weights (AULA) further refines bias evaluation.

Abstract

Masked Language Models (MLMs) have shown superior performances in numerous downstream NLP tasks when used as text encoders. Unfortunately, MLMs also demonstrate significantly worrying levels of social biases. We show that the previously proposed evaluation metrics for quantifying the social biases in MLMs are problematic due to following reasons: (1) prediction accuracy of the masked tokens itself tend to be low in some MLMs, which raises questions regarding the reliability of the evaluation metrics that use the (pseudo) likelihood of the predicted tokens, and (2) the correlation between the prediction accuracy of the mask and the performance in downstream NLP tasks is not taken into consideration, and (3) high frequency words in the training data are masked more often, introducing noise due to this selection bias in the test cases. To overcome the above-mentioned disfluencies, we propose All Unmasked Likelihood (AUL), a bias evaluation measure that predicts all tokens in a test case given the MLM embedding of the unmasked input. We find that AUL accurately detects different types of biases in MLMs. We also propose AUL with attention weights (AULA) to evaluate tokens based on their importance in a sentence. However, unlike AUL and AULA, previously proposed bias evaluation measures for MLMs systematically overestimate the measured biases, and are heavily influenced by the unmasked tokens in the context.