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.
Abstract (translated)
语言技术已经取得了巨大的进步，特别是随着大型语言模型（LLMs）的引入。在这些传统任务（如机器翻译和情感分析）中，这些模型表现接近人类水平。然而，这些进步也可能加剧模型长期以来一直难以解决的问题，例如偏见、评估和风险。在本文论文中，我们认为许多这些问题共享一个共同核心：对自然语言处理操作的社会环境因素、上下文和影响的缺乏认识，我们称之为社会意识。虽然自然语言处理在解决形式语言方面正在取得进步，但在添加所需的社交意识以使语言应用在所有情况和所有用户中正常运行方面，进展有限。将社会意识集成到自然语言处理模型中，将使应用更加自然、有益和安全，并开辟新的可能性。因此，我们认为在NLP开发社会意识方面仍然存在巨大的挑战，我们刚刚进入该领域的新的时代。
URL
https://arxiv.org/abs/2405.02411
