Language models are trained on large-scale corpora that embed implicit biases
documented in psychology. Valence associations (pleasantness/unpleasantness) of
social groups determine the biased attitudes towards groups and concepts in
social cognition. Building on this established literature, we quantify how
social groups are valenced in English language models using a sentence template
that provides an intersectional context. We study biases related to age,
education, gender, height, intelligence, literacy, race, religion, sex, sexual
orientation, social class, and weight. We present a concept projection approach
to capture the valence subspace through contextualized word embeddings of
language models. Adapting the projection-based approach to embedding
association tests that quantify bias, we find that language models exhibit the
most biased attitudes against gender identity, social class, and sexual
orientation signals in language. We find that the largest and better-performing
model that we study is also more biased as it effectively captures bias
embedded in sociocultural data. We validate the bias evaluation method by
overperforming on an intrinsic valence evaluation task. The approach enables us
to measure complex intersectional biases as they are known to manifest in the
outputs and applications of language models that perpetuate historical biases.
Moreover, our approach contributes to design justice as it studies the
associations of groups underrepresented in language such as transgender and
homosexual individuals.

利用上下文词嵌入的概念投射方法，量化了英语语言模型中社会群体的情感倾向，发现语言模型对性别认同、社会阶级和性取向的信号表现出最有偏见的态度，此方法旨在研究语言模型中的历史偏见，并对设计正义做出贡献，探讨了在语言中被边缘化的群体的相关关系。

在交叉背景下评估语言模型的偏见态度关联

Evaluating Biased Attitude Associations of Language Models in an  Intersectional Context

We propose a novel, projection based way to incorporate the conditional
information into the discriminator of GANs that respects the role of the
conditional information in the underlining probabilistic model. This approach
is in contrast with most frameworks of conditional GANs used in application
today, which use the conditional information by concatenating the (embedded)
conditional vector to the feature vectors. With this modification, we were able
to significantly improve the quality of the class conditional image generation
on ILSVRC2012 (ImageNet) 1000-class image dataset from the current
state-of-the-art result, and we achieved this with a single pair of a
discriminator and a generator. We were also able to extend the application to
super-resolution and succeeded in producing highly discriminative
super-resolution images. This new structure also enabled high quality category
transformation based on parametric functional transformation of conditional
batch normalization layers in the generator.

本文提出了一种新的投影方法来将条件信息纳入 GAN 的鉴别器中，以尊重条件信息在概率模型中的作用，这种方法与当前应用中使用嵌入向量将条件信息与特征向量拼接的大多数条件 GAN 框架形成对比。通过这种修改，我们能够显著提高基于 ILSVRC2012（ImageNet）1000 类图像数据集的类别条件图像生成质量，并且使用单个鉴别器和生成器对此进行了实现。我们还将这种应用扩展到超分辨率，并成功地生成了高度有区别的超分辨率图像。这种新的结构还使基于参数化功能转换的条件批归一化层的高质量类别转换成为可能。