mlsanityData to 14 Oct 2026

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

arXiv 2407.13999 ↗First posted 19 Jul 2024cs.CLcited by 8

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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.

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

26 of 72 references.

References · 26

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  6. 2020Compositionality and Generalization in Emergent Languagescited by 103
  7. 2018Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Inputcited by 102
  8. 2017Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialogcited by 86
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14 more
  1. 2019Emergent Linguistic Phenomena in Multi-Agent Communication Gamescited by 36
  2. 2022Emergent Communication: Generalization and Overfitting in Lewis Gamescited by 28
  3. 2020"LazImpa": Lazy and Impatient neural agents learn to communicate efficientlycited by 23
  4. 2022Emergent Communication for Understanding Human Language Evolution: What's Missing?cited by 23
  5. 2017The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languagescited by 19
  6. 2019Word-order biases in deep-agent emergent communicationcited by 18
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  8. 2019Shaping representations through communication: community size effect in artificial learning systemscited by 14
  9. 2021Conceptual similarity and communicative need shape colexification: an experimental studycited by 14
  10. 2023Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-offcited by 12
  11. 2024Emergent Word Order Universals from Cognitively-Motivated Language Modelscited by 11
  12. 2021On the Difficulty of Translating Free-Order Case-Marking Languagescited by 8
  13. 2024Learning and communication pressures in neural networks: Lessons from emergent communicationcited by 8
  14. 2021The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learningcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 3.

8 papers.

  1. 2024Searching for Structure: Investigating Emergent Communication with Large Language Modelscited by 10
  2. 2025Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interactioncited by 9
  3. 2025Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agentscited by 5
  4. 2025NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Usecited by 2
  5. 2025Discovering Properties of Inflectional Morphology in Neural Emergent Communicationcited by 0
  6. 2026Modeling Human-Like Color Naming Behavior in Contextcited by 0
  7. 2026Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Modelscited by 0
  8. 2026Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulationscited by 0