Indeed, language models are biased in a number of ways by the distributions of their training data. Gehman et al. (2020) shows that large language model training datasets include toxic text scraped from banned sites. In addition to problems of toxicity, internet data is disproportionately generated by authors from developed countries, and many large language models train on data from Reddit, whose authors skew male and young. Such biased population samples likely skew the resulting generation away from the perspectives or topics of underrepresented populations. Furthermore, language models can amplify demographic and other biases in training data, just as we saw for embedding models in Chapter 6.