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

2025-12-01

AI Summary

This study addresses the problem of unsupervised acquisition and explicit modeling of linguistic syntactic knowledge. To this end, we propose MODOMA, a multi-agent simulation framework that emulates adult–child interactions to enable data-free language acquisition. We further introduce the Knowledge-based Language Model (KLM), which explicitly represents and parametrically learns grammatical categories—such as functional vs. lexical classes—as interpretable, manipulable structured knowledge. Our method integrates statistical induction with rule-guided learning, enabling fully controllable experiments and faithful replication of human-like developmental patterns. Empirical results demonstrate that child agents consistently induce grammatical categories across varying sample sizes; the KLM exhibits both fluent novel sentence generation and accurate structural parsing. Collectively, this work establishes a novel paradigm for building interpretable, evolvable language models grounded in cognitively plausible mechanisms.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Developing a multi-agent simulation for unsupervised language acquisition
• Creating a knowledge-based model to generate and parse new utterances
• Acquiring and representing grammatical categories using machine-generated data

Innovation

• Multi-agent simulation for unsupervised language acquisition
• Knowledge-based language model using statistical and rule-based methods
• Parametrized system enabling explicit representation of grammatical knowledge