The paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" by Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell explores the potential risks associated with the development and deployment of increasingly large language models (LMs). The authors argue that while large LMs have achieved significant advancements in various NLP tasks, they also pose environmental, financial, and social risks. They recommend prioritizing the environmental and financial costs of training and deploying these models, curating and documenting datasets carefully, and avoiding the misinterpretation of LM-driven performance gains as actual natural language understanding. The paper highlights the overrepresentation of hegemonic viewpoints in large, uncurated datasets, which can encode biases and harmful stereotypes. It also discusses the risks of automation bias, deliberate misuse, and the amplification of hegemonic worldviews when LM outputs are used in generating text or other applications. The authors advocate for a more equitable and sustainable approach to NLP research and development, emphasizing the need for deeper understanding and more diverse and inclusive datasets.The paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" by Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell explores the potential risks associated with the development and deployment of increasingly large language models (LMs). The authors argue that while large LMs have achieved significant advancements in various NLP tasks, they also pose environmental, financial, and social risks. They recommend prioritizing the environmental and financial costs of training and deploying these models, curating and documenting datasets carefully, and avoiding the misinterpretation of LM-driven performance gains as actual natural language understanding. The paper highlights the overrepresentation of hegemonic viewpoints in large, uncurated datasets, which can encode biases and harmful stereotypes. It also discusses the risks of automation bias, deliberate misuse, and the amplification of hegemonic worldviews when LM outputs are used in generating text or other applications. The authors advocate for a more equitable and sustainable approach to NLP research and development, emphasizing the need for deeper understanding and more diverse and inclusive datasets.