Searching for the Most Human-like Emergent Language

2025-10-03

AI Summary

This study aims to generate emergent languages whose statistical properties closely approximate those of natural human language. Method: We propose a novel paradigm that uses transfer learning performance—specifically, XferBench accuracy on deep transfer tasks—as a metric for linguistic similarity. Within a neural communication framework grounded in signaling games, we perform systematic hyperparameter optimization targeting this objective. Information-theoretic analysis, particularly entropy profiling, is integrated to interpret structural properties. Contribution/Results: We find that low-entropy linguistic structures significantly enhance cross-task transferability, validating entropy as a principled indicator of evolutionary advantage. Moreover, we identify specific hyperparameter configurations that jointly strengthen word-order regularity, frequency distributions (e.g., Zipf’s law), and hierarchical syntax—core hallmarks of human language. Empirically, the resulting emergent languages outperform baselines across multiple dimensions, including Zipfian distribution fidelity, entropy profiles, and syntactic transferability. This work establishes an interpretable, optimization-driven framework for artificial language evolution and cross-modal semantic alignment.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Evolutionary Learning
• Humans and AI: Learning Human Values and Preferences

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Generating emergent languages highly similar to human language
• Quantifying statistical similarity via deep transfer learning performance
• Identifying hyperparameters that produce human-like emergent languages

Innovation

• Signaling game environment generates human-like emergent languages
• Hyperparameter optimization uses XferBench as objective function
• Entropy predicts transfer learning performance of emergent languages