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 risks associated with the increasing size of language models (LMs) in natural language processing (NLP). The authors argue that while larger LMs have improved performance on various tasks, they also pose significant environmental, financial, and social risks. These include high energy consumption, environmental impact, and the potential for reinforcing biases and harmful stereotypes in training data. The paper highlights that the training data for LMs often reflects dominant, hegemonic viewpoints, which can lead to the encoding of biases and stereotypes that harm marginalized communities. Large LMs trained on such data may produce text that is perceived as meaningful, but it lacks genuine understanding of language and context. This can lead to the misinterpretation of LM-generated text as meaningful, even when it is not, potentially causing harm through the spread of biased or offensive content. The authors recommend that researchers should prioritize environmental and financial costs, invest in curating and documenting datasets, and evaluate how LM development aligns with research and development goals. They also emphasize the importance of understanding the limitations of LMs and encouraging research directions that do not rely solely on increasing model size. The paper calls for a more equitable approach to NLP research, focusing on the ethical implications of LM development and the need to mitigate the risks associated with biased and harmful outputs.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 risks associated with the increasing size of language models (LMs) in natural language processing (NLP). The authors argue that while larger LMs have improved performance on various tasks, they also pose significant environmental, financial, and social risks. These include high energy consumption, environmental impact, and the potential for reinforcing biases and harmful stereotypes in training data. The paper highlights that the training data for LMs often reflects dominant, hegemonic viewpoints, which can lead to the encoding of biases and stereotypes that harm marginalized communities. Large LMs trained on such data may produce text that is perceived as meaningful, but it lacks genuine understanding of language and context. This can lead to the misinterpretation of LM-generated text as meaningful, even when it is not, potentially causing harm through the spread of biased or offensive content. The authors recommend that researchers should prioritize environmental and financial costs, invest in curating and documenting datasets, and evaluate how LM development aligns with research and development goals. They also emphasize the importance of understanding the limitations of LMs and encouraging research directions that do not rely solely on increasing model size. The paper calls for a more equitable approach to NLP research, focusing on the ethical implications of LM development and the need to mitigate the risks associated with biased and harmful outputs.