Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias

By Yarden Tal, Inbal Magar, Roy Schwartz. 2022

45 citations recorded by the source corpus.

The size of pretrained models is increasing, and so is their performance on a variety of NLP tasks. However, as their memorization capacity grows, they might pick up more social biases. In this work, we examine the connection between model size and its gender bias (specifically, occupational gender bias). We measure bias in three masked language model families (RoBERTa, DeBERTa, and T5) in two setups: directly using prompt based method, and using a downstream task (Winogender). We find on the one hand that larger models receive higher bias scores on the former task, but when evaluated on the latter, they make fewer gender errors. To examine these potentially conflicting results, we carefully investigate the behavior of the different models on Winogender. We find that while larger models outperform smaller ones, the probability that their mistakes are caused by gender bias is higher. Moreover, we find that the proportion of stereotypical errors compared to anti-stereotypical ones grows with the model size. Our findings highlight the potential risks that can arise from increasing model size.

Subfields: Artificial Intelligence, Large Language Models, Safety, Robustness & Interpretability

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Citations in the corpus

Llama 2: Open Foundation and Fine-Tuned Chat Models (2023, background): “, 2021b), but empirical work to validate this claim is still ongoing (Dodge et al., 2021; Smith and Williams, 2021; Tal et al., 2022), and further evidence from up-to-date models is still needed.”
Training Trajectories of Language Models Across Scales (2022, background): “Previous work studies scaling on downstream tasks (Wei et al., 2022; Srivastava et al., 2022), pre-training data (Hernandez et al., 2022), architectures (Tay et al., 2022), biases (Tal et al., 2022), and other domains, such as vision tasks and neural machine translation (Alabdulmohsin et al., 2022).”