mlsanityData to 7 Oct 2026

A Knowledge-Based Language Model: Deducing Grammatical Knowledge in a Multi-Agent Language Acquisition Simulation

arXiv 2512.02195 ↗First posted 1 Dec 2025cs.CL cs.AI cs.LGcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

This paper presents an initial study performed by the MODOMA system. The MODOMA is a computational multi-agent laboratory environment for unsupervised language acquisition experiments such that acquisition is based on the interaction between two language models, an adult and a child agent. Although this framework employs statistical as well as rule-based procedures, the result of language acquisition is a knowledge-based language model, which can be used to generate and parse new utterances of the target language. This system is fully parametrized and researchers can control all aspects of the experiments while the results of language acquisition, that is, the acquired grammatical knowledge, are explicitly represented and can be consulted. Thus, this system introduces novel possibilities for conducting computational language acquisition experiments. The experiments presented by this paper demonstrate that functional and content categories can be acquired and represented by the daughter agent based on training and test data containing different amounts of exemplars generated by the adult agent. Interestingly, similar patterns, which are well-established for human-generated data, are also found for these machine-generated data. As the procedures resulted in the successful acquisition of discrete grammatical categories by the child agent, these experiments substantiate the validity of the MODOMA approach to modelling language acquisition.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 12 of its 49 references that are in the map. First posted on arXiv 1 Dec 2025.

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References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  4. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
  5. 2020Compositionality and Generalization in Emergent Languagescited by 103
  6. 2020Self-supervised learning through the eyes of a childcited by 61
  7. 2019Anti-efficient encoding in emergent communicationcited by 59
  8. 2020"LazImpa": Lazy and Impatient neural agents learn to communicate efficientlycited by 23
  9. 2019Evaluating Computational Language Models with Scaling Properties of Natural Languagecited by 20
  10. 2022Usage-based learning of grammatical categoriescited by 3
  11. 2023Do learned speech symbols follow Zipf's law?cited by 3
  12. 2025Unsupervised Acquisition of Discrete Grammatical Categoriescited by 1

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