Humans align to one another in conversation -- adopting shared conventions that ease communication. We test whether LLMs form the same kinds of conventions in a multimodal communication game. Both humans and LLMs display evidence of convention-formation (increasing the accuracy and consistency of their turns while decreasing their length) when communicating in same-type dyads (humans with humans, AI with AI). However, heterogenous human-AI pairs fail -- suggesting differences in communicative tendencies. In Experiment 2, we ask whether LLMs can be induced to behave more like human conversants, by prompting them to produce superficially humanlike behavior. While the length of their messages matches that of human pairs, accuracy and lexical overlap in human-LLM pairs continues to lag behind that of both human-human and AI-AI pairs. These results suggest that conversational alignment requires more than just the ability to mimic previous interactions, but also shared interpretative biases toward the meanings that are conveyed.

翻译：人类在对话中会相互协调——采用共享的规范以促进沟通。本研究通过多模态交流游戏测试大语言模型是否能形成同类规范。当同类配对（人类与人类、AI与AI）进行交流时，人类与大语言模型均表现出规范形成迹象（回合准确性与一致性提升，同时长度缩短）。然而，异质性的人机配对未能达成规范——这表明两者存在沟通倾向性差异。在实验2中，我们通过提示大语言模型产生表面类人行为，探究其能否被引导至更接近人类对话者的行为模式。虽然人机配对的信息长度与人类配对相当，但其准确性与词汇重叠度仍持续落后于人类-人类及AI-AI配对。这些结果表明，对话协调不仅需要模仿既往互动的能力，更依赖于对话义传达的共同解释偏向。