The paper "The Call for Socially Aware Language Technologies" by Diyi Yang discusses the need for language technologies to incorporate social awareness, which refers to the ability to understand and respond to social factors, context, and implications in human communication. While large language models (LLMs) have made significant progress in tasks like machine translation and sentiment analysis, they often fail to address issues such as bias, toxicity, and trust due to a lack of social awareness. The authors argue that social awareness is crucial for NLP to work effectively in diverse social contexts and for all users. They propose that integrating social awareness into NLP models will make applications more natural, helpful, and safe, and open up new possibilities. The paper outlines three key aspects of socially aware NLP: social factors, social interaction, and social implication. It also highlights the importance of ethical considerations and responsible development processes in building these technologies. The authors call for a unified subfield of "socially aware language technologies" to address the challenges of embedding social intelligence into language models and facilitate more precise communication among scientists, policymakers, and the public.The paper "The Call for Socially Aware Language Technologies" by Diyi Yang discusses the need for language technologies to incorporate social awareness, which refers to the ability to understand and respond to social factors, context, and implications in human communication. While large language models (LLMs) have made significant progress in tasks like machine translation and sentiment analysis, they often fail to address issues such as bias, toxicity, and trust due to a lack of social awareness. The authors argue that social awareness is crucial for NLP to work effectively in diverse social contexts and for all users. They propose that integrating social awareness into NLP models will make applications more natural, helpful, and safe, and open up new possibilities. The paper outlines three key aspects of socially aware NLP: social factors, social interaction, and social implication. It also highlights the importance of ethical considerations and responsible development processes in building these technologies. The authors call for a unified subfield of "socially aware language technologies" to address the challenges of embedding social intelligence into language models and facilitate more precise communication among scientists, policymakers, and the public.