# Emergent Language: A Survey and Taxonomy arXiv: https://arxiv.org/abs/2409.02645 ยท HTML: https://mlsanity.com/paper/2409.02645/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. - Builds on 34 of its 73 references that are in the map. - First posted on arXiv 4 Sep 2024. ## Placed in - Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/) ## Builds on (most cited first, 30 of 34) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023) - [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024) - [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020) - [The Hanabi Challenge: A New Frontier for AI Research](https://mlsanity.com/paper/1902.00506/) (2019) - [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018) - [Natural Language Processing: State of The Art, Current Trends and Challenges](https://mlsanity.com/paper/1708.05148/) (2017) - [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022) - [Compositionality and Generalization in Emergent Languages](https://mlsanity.com/paper/2004.09124/) (2020) - [MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence](https://mlsanity.com/paper/1712.00600/) (2017) - [Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?](https://mlsanity.com/paper/2104.10809/) (2021) - [Morphology Matters: A Multilingual Language Modeling Analysis](https://mlsanity.com/paper/2012.06262/) (2020) - [Learning to Interactively Learn and Assist](https://mlsanity.com/paper/1906.10187/) (2019) - [Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven Communication](https://mlsanity.com/paper/1901.03887/) (2019) - [Co-evolution of language and agents in referential games](https://mlsanity.com/paper/2001.03361/) (2020) - [Emergent Language Generalization and Acquisition Speed are not tied to Compositionality](https://mlsanity.com/paper/2004.03420/) (2020) - [Symbol Emergence as an Interpersonal Multimodal Categorization](https://mlsanity.com/paper/1905.13443/) (2019) - [Internal and external pressures on language emergence: least effort, object constancy and frequency](https://mlsanity.com/paper/2004.03868/) (2020) - [Emergent Communication Pretraining for Few-Shot Machine Translation](https://mlsanity.com/paper/2011.00890/) (2020) - [The Emergence of the Shape Bias Results from Communicative Efficiency](https://mlsanity.com/paper/2109.06232/) (2021) - [Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference](https://mlsanity.com/paper/2109.07194/) (2021) - [The Grammar of Emergent Languages](https://mlsanity.com/paper/2010.02069/) (2020) - [Defending Compositionality in Emergent Languages](https://mlsanity.com/paper/2206.04751/) (2022) - [Emergent Quantized Communication](https://mlsanity.com/paper/2211.02412/) (2022) - [Concept-Best-Matching: Evaluating Compositionality in Emergent Communication](https://mlsanity.com/paper/2403.14705/) (2024) - [Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation](https://mlsanity.com/paper/2305.05116/) (2023) - [Mutual influence between language and perception in multi-agent communication games](https://mlsanity.com/paper/2112.14518/) (2021) - [Learning to translate by learning to communicate](https://mlsanity.com/paper/2207.07025/) (2022) - [Learning Multi-Object Positional Relationships via Emergent Communication](https://mlsanity.com/paper/2302.08084/) (2023) - [Preference Communication in Multi-Objective Normal-Form Games](https://mlsanity.com/paper/2111.09191/) (2021) ## Built on by (most cited first, 13 of 13) - [Generative Emergent Communication: Large Language Model is a Collective World Model](https://mlsanity.com/paper/2501.00226/) (2024) - [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026) - [Decentralized Collective World Model for Emergent Communication and Coordination](https://mlsanity.com/paper/2504.03353/) (2025) - [System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systems](https://mlsanity.com/paper/2503.06138/) (2025) - [From Grunts to Lexicons: Emergent Language from Cooperative Foraging](https://mlsanity.com/paper/2505.12872/) (2025) - [Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation](https://mlsanity.com/paper/2508.05535/) (2025) - [VQEL: Enabling Self-Play in Emergent Language Games via Agent-Internal Vector Quantization](https://mlsanity.com/paper/2503.04940/) (2025) - [Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI](https://mlsanity.com/paper/2506.15468/) (2025) - [Why Are Parsing Actions for Understanding Message Hierarchies Not Random?](https://mlsanity.com/paper/2506.22366/) (2025) - [Emergent Communication between Heterogeneous Visual Agents through Decentralized Learning](https://mlsanity.com/paper/2605.11695/) (2026) - [Emergent Language as an Approach to Conscious AI](https://mlsanity.com/paper/2606.06380/) (2026) - [EmCom-Diffusion: Probing Visual Reflection in Emergent Languages via Image Generation](https://mlsanity.com/paper/2607.03752/) (2026) - [On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination](https://mlsanity.com/paper/2610.18727/) (2026) ## Abstract (arXiv) The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learning. Although the concept of studying language emergence is not new, early approaches were primarily concerned with explaining human language formation, with little consideration given to its potential utility for artificial agents. In contrast, studies based on reinforcement learning aim to develop communicative capabilities in agents that are comparable to or even superior to human language. Thus, they extend beyond the learned statistical representations that are common in natural language processing research. This gives rise to a number of fundamental questions, from the prerequisites for language emergence to the criteria for measuring its success. This paper addresses these questions by providing a comprehensive review of 181 scientific publications on emergent language in artificial intelligence. Its objective is to serve as a reference for researchers interested in or proficient in the field. Consequently, the main contributions are the definition and overview of the prevailing terminology, the analysis of existing evaluation methods and metrics, and the description of the identified research gaps.