用游戏生成接近人类语言的智能体通信系统，提升跨模型迁移能力。
Searching for the Most Human-like Emergent Language
- 设计信号博弈环境，通过超参数优化生成类人语言
- 使用XferBench评估语言迁移性能，发现熵越低迁移越强
- 揭示可使语言更真实的关键超参数配置，适合语言演化研究者
本文设计了一种基于信号博弈的涌现通信环境，通过超参数优化生成在统计上最接近人类语言的先进涌现语言。以XferBench作为目标函数，该指标通过衡量涌现语言在深度迁移学习中对人类语言的适用性，量化其与人类语言的相似程度。此外，我们验证了熵对涌现语言迁移性能的预测作用，并再次支持了涌现通信系统具有熵最小化特性的已有结论。最后，我们总结出能生成更真实、迁移能力更强语言的超参数规律，为理解智能体语言演化提供实证依据。
原文摘要 · Abstract (English)
In this paper, we design a signalling game-based emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language. This is done with hyperparameter optimization, using XferBench as the objective function. XferBench quantifies the statistical similarity of emergent language to human language by measuring its suitability for deep transfer learning to human language. Additionally, we demonstrate the predictive power of entropy on the transfer learning performance of emergent language as well as corroborate previous results on the entropy-minimization properties of emergent communication systems. Finally, we report generalizations regarding what hyperparameters produce more realistic emergent languages, that is, ones which transfer better to human language.
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