探寻结构：基于大语言模型的涌现通信研究
Searching for Structure: Investigating Emergent Communication with Large Language Models
AI总结：
本研究通过指称博弈模拟探究大语言模型的内隐偏差是否会推动人工语言结构化演化，发现无结构语言会形成结构化属性以支撑智能体通信，代际传递可提升语言可学性但可能产生退化词汇，验证了LLM在语言演化模拟中的工具价值。
摘要：
人类语言通过反复的语言学习与使用逐渐演化出结构化特征。这些过程会在语言习得阶段引入认知偏差，推动语言系统向具备交际效率的方向发展。本文探究当人工语言针对大语言模型（LLM）的内隐偏差进行优化时，是否会出现同样的演化现象。为此，我们模拟了经典的指称博弈任务，让LLM在其中学习并使用人工语言。研究结果表明，初始阶段无结构的整体性语言确实会被塑造出部分结构化属性，使两个LLM智能体能够成功完成通信。与人类实验中的观测结果相似，代际传递提升了语言的可学习性，但同时也可能产生不符合人类语言特征的退化词汇表。综上，本研究拓展了现有实验结论，证明LLM可作为工具用于语言演化模拟，并为该领域未来的人机交互实验开辟了可能。
英文摘要：
Human languages have evolved to be structured through repeated language learning and use. These processes introduce biases that operate during language acquisition and shape linguistic systems toward communicative efficiency. In this paper, we investigate whether the same happens if artificial languages are optimised for implicit biases of Large Language Models (LLMs). To this end, we simulate a classical referential game in which LLMs learn and use artificial languages. Our results show that initially unstructured holistic languages are indeed shaped to have some structural properties that allow two LLM agents to communicate successfully. Similar to observations in human experiments, generational transmission increases the learnability of languages, but can at the same time result in non-humanlike degenerate vocabularies. Taken together, this work extends experimental findings, shows that LLMs can be used as tools in simulations of language evolution, and opens possibilities for future human-machine experiments in this 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。
