BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation

• URL: http://arxiv.org/abs/2210.07626v1
• Date: Fri, 14 Oct 2022 08:24:11 GMT
• Title: BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation
• Authors: Tianxiang Sun, Junliang He, Xipeng Qiu, Xuanjing Huang
• Abstract summary: This work presents the first systematic study on the social bias in PLM-based metrics. We demonstrate that popular PLM-based metrics exhibit significantly higher social bias than traditional metrics on 6 sensitive attributes. In addition, we develop debiasing adapters that are injected into PLM layers, mitigating bias in PLM-based metrics while retaining high performance for evaluating text generation.
• Score: 89.41378346080603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic evaluation metrics are crucial to the development of generative systems. In recent years, pre-trained language model (PLM) based metrics, such as BERTScore, have been commonly adopted in various generation tasks. However, it has been demonstrated that PLMs encode a range of stereotypical societal biases, leading to a concern on the fairness of PLMs as metrics. To that end, this work presents the first systematic study on the social bias in PLM-based metrics. We demonstrate that popular PLM-based metrics exhibit significantly higher social bias than traditional metrics on 6 sensitive attributes, namely race, gender, religion, physical appearance, age, and socioeconomic status. In-depth analysis suggests that choosing paradigms (matching, regression, or generation) of the metric has a greater impact on fairness than choosing PLMs. In addition, we develop debiasing adapters that are injected into PLM layers, mitigating bias in PLM-based metrics while retaining high performance for evaluating text generation.

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