《对具备社会感知的语言技术的呼吁》
The Call for Socially Aware Language Technologies
AI总结：
本文指出当前大语言模型虽在传统NLP任务上接近人类水平，但因缺乏社会感知加剧了偏见等问题，呼吁NLP领域发展社会感知技术，以提升应用的自然性、实用性与安全性，开启领域发展新时代。
摘要：
语言技术已取得巨大进展，尤其是随着大语言模型（LLMs）的推出。在机器翻译、情感分析等传统任务上，这些模型的表现已接近人类水平。然而，这些进展可能会加剧模型历来难以应对的各类问题，比如偏见、评估以及风险问题。在这篇立场论文中，我们提出这类问题大多有一个共同核心：自然语言处理（NLP）系统对其运行所处社会环境的影响因素、语境及相关影响缺乏认知，我们将这种认知称为社会感知。尽管NLP在解决形式语言学层面的问题上表现越来越好，但在融入语言应用所需的社会感知方面进展有限，而社会感知是语言应用能在所有场景下为所有用户正常运行的必要条件。将社会感知融入NLP模型将让应用更自然、更实用、更安全，还将开辟新的可能性。因此我们认为，NLP要发展出社会感知仍存在大量挑战，我们正处于该领域一个新时代的开端。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
