An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models

Large-scale Pre-Trained Language Models (PTLMs) capture knowledge from massive human-written data which contains latent societal biases and toxic contents. In this paper, we leverage the primary task of PTLMs, i.e., language modeling, and propose a new metric to quantify manifested implicit representational harms in PTLMs towards 13 marginalized demographics. Using this metric, we conducted an empirical analysis of 24 widely used PTLMs. Our analysis provides insights into the correlation between the proposed metric in this work and other related metrics for representational harm. We observe that our metric correlates with most of the gender-specific metrics in the literature. Through extensive experiments, we explore the connections between PTLMs architectures and representational harms across two dimensions: depth and width of the networks. We found that prioritizing depth over width, mitigates representational harms in some PTLMs. Large-scale Pre-Trained Language Models (PTLMs) such as BERT (Devlin et al., 2019) and GPT models (Radford et al., 2019; Brown et al., 2020) have recently achieved great success in varieties of Natural Language Processing (NLP) tasks. These large-scale PTLMs capture knowledge from massively labeled and unlabeled human written data which can potentially contain harmful contents and societal biases. The goal of a language model is to estimate the probability of a sequence of words for the given language. One can argue that, when the data from which the model was trained on is different than the desired behavior of the model at a semantic level, representational harms are present.