# Emergent Communication for Understanding Human Language Evolution: What's Missing? arXiv: https://arxiv.org/abs/2204.10590 ยท HTML: https://mlsanity.com/paper/2204.10590/ ## Key facts - Cited by 23 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 52 references that are in the map. - First posted on arXiv 22 Apr 2022. ## Builds on (most cited first, 16 of 16) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019) - [Multi-Agent Cooperation and the Emergence of (Natural) Language](https://mlsanity.com/paper/1612.07182/) (2016) - [Emergent Multi-Agent Communication in the Deep Learning Era](https://mlsanity.com/paper/2006.02419/) (2020) - [Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols](https://mlsanity.com/paper/1705.11192/) (2017) - [Compositionality and Generalization in Emergent Languages](https://mlsanity.com/paper/2004.09124/) (2020) - [Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input](https://mlsanity.com/paper/1804.03984/) (2018) - [Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog](https://mlsanity.com/paper/1706.08502/) (2017) - [Ease-of-Teaching and Language Structure from Emergent Communication](https://mlsanity.com/paper/1906.02403/) (2019) - [Anti-efficient encoding in emergent communication](https://mlsanity.com/paper/1905.12561/) (2019) - [Emergent Linguistic Phenomena in Multi-Agent Communication Games](https://mlsanity.com/paper/1901.08706/) (2019) - [Capacity, Bandwidth, and Compositionality in Emergent Language Learning](https://mlsanity.com/paper/1910.11424/) (2019) - [The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents](https://mlsanity.com/paper/1910.05291/) (2019) - ["LazImpa": Lazy and Impatient neural agents learn to communicate efficiently](https://mlsanity.com/paper/2010.01878/) (2020) - [Shaping representations through communication: community size effect in artificial learning systems](https://mlsanity.com/paper/1912.06208/) (2019) ## Built on by (most cited first, 23 of 23) - [Context Limitations Make Neural Language Models More Human-Like](https://mlsanity.com/paper/2205.11463/) (2022) - [Emergent Communication: Generalization and Overfitting in Lewis Games](https://mlsanity.com/paper/2209.15342/) (2022) - [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024) - [Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models](https://mlsanity.com/paper/2205.12392/) (2022) - [What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structure](https://mlsanity.com/paper/2302.12239/) (2023) - [Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off](https://mlsanity.com/paper/2301.13083/) (2023) - [Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments](https://mlsanity.com/paper/2311.04453/) (2023) - [Emergent Word Order Universals from Cognitively-Motivated Language Models](https://mlsanity.com/paper/2402.12363/) (2024) - [Searching for Structure: Investigating Emergent Communication with Large Language Models](https://mlsanity.com/paper/2412.07646/) (2024) - [NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication](https://mlsanity.com/paper/2407.13999/) (2024) - [The Curious Case of Representational Alignment: Unravelling Visio-Linguistic Tasks in Emergent Communication](https://mlsanity.com/paper/2407.17960/) (2024) - [Language Evolution with Deep Learning](https://mlsanity.com/paper/2403.11958/) (2024) - [Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents](https://mlsanity.com/paper/2502.04038/) (2025) - [EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation](https://mlsanity.com/paper/2505.06904/) (2025) - [What does Kiki look like? Cross-modal associations between speech sounds and visual shapes in vision-and-language models](https://mlsanity.com/paper/2407.17974/) (2024) - [A framework for the emergence and analysis of language in social learning agents](https://mlsanity.com/paper/2305.02632/) (2023) - [Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion](https://mlsanity.com/paper/2605.31170/) (2026) - [On the Correspondence between Compositionality and Imitation in Emergent Neural Communication](https://mlsanity.com/paper/2305.12941/) (2023) - [XferBench: a Data-Driven Benchmark for Emergent Language](https://mlsanity.com/paper/2407.03456/) (2024) - [From Grunts to Lexicons: Emergent Language from Cooperative Foraging](https://mlsanity.com/paper/2505.12872/) (2025) - [Discovering Properties of Inflectional Morphology in Neural Emergent Communication](https://mlsanity.com/paper/2508.05843/) (2025) - [Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI](https://mlsanity.com/paper/2511.18517/) (2025) - [Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations](https://mlsanity.com/paper/2609.06025/) (2026) ## Abstract (arXiv) Emergent communication protocols among humans and artificial neural network agents do not yet share the same properties and show some critical mismatches in results. We describe three important phenomena with respect to the emergence and benefits of compositionality: ease-of-learning, generalization, and group size effects (i.e., larger groups create more systematic languages). The latter two are not fully replicated with neural agents, which hinders the use of neural emergent communication for language evolution research. We argue that one possible reason for these mismatches is that key cognitive and communicative constraints of humans are not yet integrated. Specifically, in humans, memory constraints and the alternation between the roles of speaker and listener underlie the emergence of linguistic structure, yet these constraints are typically absent in neural simulations. We suggest that introducing such communicative and cognitive constraints would promote more linguistically plausible behaviors with neural agents.