# NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication

arXiv: https://arxiv.org/abs/2407.13999 · HTML: https://mlsanity.com/paper/2407.13999/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 72 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 26 of 26)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [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)
- [Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning](https://mlsanity.com/paper/2005.07064/) (2020)
- [Ease-of-Teaching and Language Structure from Emergent Communication](https://mlsanity.com/paper/1906.02403/) (2019)
- [EGG: a toolkit for research on Emergence of lanGuage in Games](https://mlsanity.com/paper/1907.00852/) (2019)
- [Examining the Inductive Bias of Neural Language Models with Artificial Languages](https://mlsanity.com/paper/2106.01044/) (2021)
- [Emergent Linguistic Phenomena in Multi-Agent Communication Games](https://mlsanity.com/paper/1901.08706/) (2019)
- [Emergent Communication: Generalization and Overfitting in Lewis Games](https://mlsanity.com/paper/2209.15342/) (2022)
- ["LazImpa": Lazy and Impatient neural agents learn to communicate efficiently](https://mlsanity.com/paper/2010.01878/) (2020)
- [Emergent Communication for Understanding Human Language Evolution: What's Missing?](https://mlsanity.com/paper/2204.10590/) (2022)
- [The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages](https://mlsanity.com/paper/1710.03838/) (2017)
- [Word-order biases in deep-agent emergent communication](https://mlsanity.com/paper/1905.12330/) (2019)
- [A Roadmap towards Machine Intelligence](https://mlsanity.com/paper/1511.08130/) (2015)
- [Shaping representations through communication: community size effect in artificial learning systems](https://mlsanity.com/paper/1912.06208/) (2019)
- [Conceptual similarity and communicative need shape colexification: an experimental study](https://mlsanity.com/paper/2103.11024/) (2021)
- [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)
- [Emergent Word Order Universals from Cognitively-Motivated Language Models](https://mlsanity.com/paper/2402.12363/) (2024)
- [On the Difficulty of Translating Free-Order Case-Marking Languages](https://mlsanity.com/paper/2107.06055/) (2021)
- [Learning and communication pressures in neural networks: Lessons from emergent communication](https://mlsanity.com/paper/2403.14427/) (2024)
- [The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning](https://mlsanity.com/paper/2104.07637/) (2021)

## Built on by (most cited first, 8 of 8)

- [Searching for Structure: Investigating Emergent Communication with Large Language Models](https://mlsanity.com/paper/2412.07646/) (2024)
- [Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interaction](https://mlsanity.com/paper/2510.20411/) (2025)
- [Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents](https://mlsanity.com/paper/2502.04038/) (2025)
- [NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use](https://mlsanity.com/paper/2509.22479/) (2025)
- [Discovering Properties of Inflectional Morphology in Neural Emergent Communication](https://mlsanity.com/paper/2508.05843/) (2025)
- [Modeling Human-Like Color Naming Behavior in Context](https://mlsanity.com/paper/2604.25674/) (2026)
- [Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models](https://mlsanity.com/paper/2608.15129/) (2026)
- [Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations](https://mlsanity.com/paper/2609.06025/) (2026)

## Abstract (arXiv)

Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols. The recently introduced NeLLCom framework (Lian et al., 2023) allows agents to first learn an artificial language and then use it to communicate, with the aim of studying the emergence of specific linguistics properties. We extend this framework (NeLLCom-X) by introducing more realistic role-alternating agents and group communication in order to investigate the interplay between language learnability, communication pressures, and group size effects. We validate NeLLCom-X by replicating key findings from prior research simulating the emergence of a word-order/case-marking trade-off. Next, we investigate how interaction affects linguistic convergence and emergence of the trade-off. The novel framework facilitates future simulations of diverse linguistic aspects, emphasizing the importance of interaction and group dynamics in language evolution.