In the recent shift towards human-centric AI, the need for machines to accurately use natural language has become increasingly important. While a common approach to achieve this is to train large language models, this method presents a form of learning misalignment where the model may not capture the underlying structure and reasoning humans employ in using natural language, potentially leading to unexpected or unreliable behavior. Emergent communication (Emecom) is a field of research that has seen a growing number of publications in recent years, aiming to develop artificial agents capable of using natural language in a way that goes beyond simple discriminative tasks and can effectively communicate and learn new concepts. In this review, we present Emecom under two aspects. Firstly, we delineate all the common proprieties we find across the literature and how they relate to human interactions. Secondly, we identify two subcategories and highlight their characteristics and open challenges. We encourage researchers to work together by demonstrating that different methods can be viewed as diverse solutions to a common problem and emphasize the importance of including diverse perspectives and expertise in the field. We believe a deeper understanding of human communication is crucial to developing machines that can accurately use natural language in human-machine interactions.

翻译：在近期向以人为中心的人工智能的转变中，机器准确使用自然语言的需求日益重要。尽管训练大型语言模型是实现这一目标的常见方法，但这种方法存在一种学习失调现象，即模型可能无法捕捉人类在使用自然语言时所采用的底层结构和推理方式，从而导致意外或不可靠的行为。涌现通信（Emecom）是一个近年来出版物数量不断增长的研究领域，其目标是开发能够超越简单判别任务、有效沟通并学习新概念的人工智能体，使其以更接近人类的方式使用自然语言。在本综述中，我们从两个方面介绍涌现通信：首先，我们梳理了文献中所有常见的特性及其与人类交互的关系；其次，我们识别出两个子类别，并强调其特点与待解决的挑战。我们鼓励研究者开展合作，表明不同方法可被视为同一问题的多样化解决方案，并强调在该领域中纳入多元视角和专业知识的重要性。我们认为，深入理解人类通信对于开发能够在人机交互中准确使用自然语言的机器至关重要。