Language models and humans are two types of learning systems. Finding or facilitating commonalities could enable major breakthroughs in our understanding of the acquisition and evolution of language. Many theories of language evolution rely heavily on learning biases and learning pressures. Yet due to substantial differences in learning pressures, it is questionable whether the similarity between humans and machines is sufficient for insights to carry over and to be worth testing with human participants. Here, we review the emergent communication literature, a subfield of multi-agent reinforcement learning, from a language evolution perspective. We find that the emergent communication literature excels at designing and adapting models to recover initially absent linguistic phenomena of natural languages. Based on a short literature review, we identify key pressures that have recovered initially absent human patterns in emergent communication models: communicative success, efficiency, learnability, and other psycho-/sociolinguistic factors. We argue that this may serve as inspiration for how to design language models for language acquisition and language evolution research.

翻译：语言模型和人类是两种学习系统。寻找或促进两者的共通之处，可能为理解语言习得与演化带来重大突破。许多语言演化理论高度依赖于学习偏差与学习压力。然而，由于学习压力存在显著差异，人机相似性是否足以使相关洞见具有可迁移性并值得通过人类被试进行检验，仍存疑问。本文从语言演化视角综述了涌现沟通文献——多智能体强化学习的一个子领域。我们发现，涌现沟通文献擅长设计与调整模型，以恢复自然语言中原本缺失的语言现象。基于简短的文献综述，我们识别出在涌现沟通模型中已成功恢复人类初始缺失模式的关键压力因素：沟通成功、效率、可学习性及其他心理/社会语言因素。我们主张，这可为如何设计适用于语言习得与语言演化研究的语言模型提供启发。