Testing Occupational Gender Bias In Language Models: Towards Robust Measurement And Zero-shot Debiasing

Chen Yuen, Raghuram Vethavikashini Chithrra, Mattern Justus, Sachan Mrinmaya, Mihalcea Rada, Schölkopf Bernhard, Jin Zhijing. Arxiv 2022

[Paper]    
Ethics And Bias Fine Tuning Pretraining Methods Prompting Training Techniques

Generated texts from large language models (LLMs) have been shown to exhibit a variety of harmful, human-like biases against various demographics. These findings motivate research efforts aiming to understand and measure such effects. Prior works have proposed benchmarks for identifying and techniques for mitigating these stereotypical associations. However, as recent research pointed out, existing benchmarks lack a robust experimental setup, hindering the inference of meaningful conclusions from their evaluation metrics. In this paper, we introduce a list of desiderata for robustly measuring biases in generative language models. Building upon these design principles, we propose a benchmark called OCCUGENDER, with a bias-measuring procedure to investigate occupational gender bias. We then use this benchmark to test several state-of-the-art open-source LLMs, including Llama, Mistral, and their instruction-tuned versions. The results show that these models exhibit substantial occupational gender bias. We further propose prompting techniques to mitigate these biases without requiring fine-tuning. Finally, we validate the effectiveness of our methods through experiments on the same set of models.

Similar Work