Text-to-image generative models have achieved unprecedented success in generating high-quality images based on natural language descriptions. However, it is shown that these models tend to favor specific social groups when prompted with neutral text descriptions (e.g., 'a photo of a lawyer'). Following Zhao et al. (2021), we study the effect on the diversity of the generated images when adding ethical intervention that supports equitable judgment (e.g., 'if all individuals can be a lawyer irrespective of their gender') in the input prompts. To this end, we introduce an Ethical NaTural Language Interventions in Text-to-Image GENeration (ENTIGEN) benchmark dataset to evaluate the change in image generations conditional on ethical interventions across three social axes -- gender, skin color, and culture. Through ENTIGEN framework, we find that the generations from minDALL.E, DALL.E-mini and Stable Diffusion cover diverse social groups while preserving the image quality. Preliminary studies indicate that a large change in the model predictions is triggered by certain phrases such as 'irrespective of gender' in the context of gender bias in the ethical interventions. We release code and annotated data at https://github.com/Hritikbansal/entigen_emnlp.

通过在输入提示中添加伦理干预来支持公平判断，我们研究了文本描述生成模型在生成图像时对特定社会群体的偏好，并使用 ENTIGEN 数据集评估伦理干预对图像生成的影响。我们发现，使用 ENTIGEN 框架生成的图像涵盖了不同的社会群体，并在保持图像质量的同时避免了社会偏见，并发现特定短语在伦理干预中的使用会触发模型预测的大幅变化。

文本到图像生成模型能多好地理解伦理自然语言干预？