The Call for Socially Aware Language Technologies

TL;DR

This paper emphasizes the importance of integrating social awareness into NLP models to address issues like bias and safety, arguing that current models lack understanding of social context, which is crucial for more natural and responsible language technologies.

Contribution

It highlights the need for social awareness in NLP and advocates for research to incorporate social context understanding into language models.

Findings

Current NLP models lack social awareness, leading to biases and safety issues.

Integrating social awareness can improve NLP applications' safety and naturalness.

Addressing social factors is essential for the future development of responsible language technologies.

Abstract

Language technologies have made enormous progress, especially with the introduction of large language models (LLMs). On traditional tasks such as machine translation and sentiment analysis, these models perform at near-human level. These advances can, however, exacerbate a variety of issues that models have traditionally struggled with, such as bias, evaluation, and risks. In this position paper, we argue that many of these issues share a common core: a lack of awareness of the factors, context, and implications of the social environment in which NLP operates, which we call social awareness. While NLP is getting better at solving the formal linguistic aspects, limited progress has been made in adding the social awareness required for language applications to work in all situations for all users. Integrating social awareness into NLP models will make applications more natural, helpful, and safe, and will open up new possibilities. Thus we argue that substantial challenges remain for NLP to develop social awareness and that we are just at the beginning of a new era for the field.