Emergence of Machine Language in LLM-based Agent Communication

Xinkun Zou, Siyue Ren, Yang Chen, Zhen Wang, Shuyue Hu

Keywords: language emergence, large language model

Abstract:

Language emergence is a hallmark of human intelligence, as well as a key indicator for assessing artificial intelligence. Unlike prior studies grounded in multi-agent reinforcement learning, this paper asks whether machine language, potentially not human-interpretable, can emerge between large language model (LLM) agents. We study this in the stylish paradigm of referential games, where a speaker describes a target object into a message with a predefined alphabet, and a listener, given the message, must identify the target among distractors. We propose an agent design that enables the speaker to retrieve semantically similar words before composing a message, and the listener to decode the message based on structural proximity between words. We observe that even given a set of 541 objects, the two agents successfully develop a shared language: they acquire meanings for each object through only 4 rounds of communication, with at most 3 attempts per communication. Additionally, analyses reveal that the emergent language
exhibits compositionality, generalizability, morphemes, and polysemy, which are defining features of human language. Our project can be accessed via the following link: https://anonymous.4open.science/r/ELofLLM-1746/

Primary Area: foundation or frontier models, including LLMs

Submission Number: 3748