An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models (2023)

Abstract: 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. Our code and data can be found at [place holder].

## introduction large-scale pre-trained language models (ptlms) such as bert @xcite and gpt models @xcite 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 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. several recent studies have highlighted the manifestation of societal biases in language models and proposed metrics and datasets to quantify them based on sentiment @xcite , regard @xcite , stereotypes @xcite , style @xcite , or morality @xcite . in this work, we focus on the ptlms' propensity to associate specific individuals or groups with negative perception. these negative perceptions are the result of microaggression, stereotypes, or implicit hate speech in the pre-training corpus of large language models. these harmful representations are usually overlooked by toxic language detectors @xcite @xcite , while they can resurface in language technologies and disadvantage an already disadvantaged group of people. moreover, existing metrics usually fail at conceptualization of these harms which is a prerequisite for effective measurement. and even when the desired construct is clearly articulated, its measurement is not well matched to its conceptualization @xcite . our contributions are two folds. first, we provide a clear conceptualization of representational harms towards 13 marginalized demographics and propose a new metric for quantifying them in ptlms. our proposed metric can be applied to any dataset that contains harmful versus benign examples. moreover, we address some of the shortcomings in the existing metrics in our metric. second, we conduct an empirical study of the representational harms in 24 well-known ptlms with respect to demographic, correlation with existing metrics, and network architecture. ## related work several metrics have been introduced to identify or measure representational harms in ptlms or their downstream applications. we categorized these metrics into extrinsic and intrinsic approaches where extrinsic metrics are associated with a downstream application and intrinsic metrics are embed-ded in the contextual representation of words and sentences. ## measurement modeling we are going to follow the measurement modeling approach, originated from social sciences, to quantify representational harms in ptlms based on @xcite recommendation. measurement modeling is composed of two stages. the first stage is conceptualization and clarifying what entity is being measured. the second stage is operationalization, which explains how this entity is being measured. ## conclusion this work presented an empirical study of representational harms in ptlms using a new metric which is based on language modeling objective and implicit toxicity. our experiments highlighted that ptlms have higher tendencies to manifest representational harms towards some marginalized demographics than others. some of these groups have not been well studied in representational harm literature such as middle eastern, hispanic, and people with disability. the correlation study between related representational harm metrics confirms that our metric is quantifying a different notion of representational harms compare to the existing metrics which is toxicity. we also observed that, this notion of representational harms is overlooked by the existing toxic language detection models. we conducted an ablation study to understand the effect of ptlms size and architecture on our safety score. our findings are; first, we should prioritize depth over width in auto-encoder models for both better downstream nlu task performance and reducing representational harms. second, in autoregressive models, there exist a trade-off between the language modeling downstream tasks and representational harms. having more depth does not hurt the safety score. however, the wider is the network, the more capable it is in manifesting implicit hate. finally, our work is a complementary step to the existing effort in expanding the notion of representational harms metrics. our work can be extended in multiple ways. first, safety score can be used as an objective function to reduce implicit hate. second, our evaluation dataset can be extended to have more examples for intersections of marginalized demographics such as middle eastern women.