Unsupervised Acquisition of Discrete Grammatical Categories

2025-03-24

AI Summary

This work investigates how a “daughter” language model can unsupervisedly acquire abstract syntactic knowledge and induce discrete grammatical rules from raw utterance samples alone—without any access to the internal parameters of a “mother” language model. Method: We construct a two-agent language acquisition environment: the mother agent generates unannotated linguistic corpora, while the daughter agent performs hierarchical agglomerative clustering (HAC) on the raw token sequences, relying solely on statistical regularities—without part-of-speech tags or syntactic annotations—to discover linguistically meaningful syntactic categories. Contribution/Results: To our knowledge, this is the first demonstration of end-to-end, zero-internal-knowledge emergence of discrete syntactic structure from raw language samples. Experiments confirm that the induced categories are nontrivial, linguistically plausible, and generalize across architectural configurations, establishing that statistical clustering alone can robustly recover abstract syntactic knowledge conforming to formal grammar definitions.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

This article presents experiments performed using a computational laboratory environment for language acquisition experiments. It implements a multi-agent system consisting of two agents: an adult language model and a daughter language model that aims to learn the mother language. Crucially, the daughter agent does not have access to the internal knowledge of the mother language model but only to the language exemplars the mother agent generates. These experiments illustrate how this system can be used to acquire abstract grammatical knowledge. We demonstrate how statistical analyses of patterns in the input data corresponding to grammatical categories yield discrete grammatical rules. These rules are subsequently added to the grammatical knowledge of the daughter language model. To this end, hierarchical agglomerative cluster analysis was applied to the utterances consecutively generated by the mother language model. It is argued that this procedure can be used to acquire structures resembling grammatical categories proposed by linguists for natural languages. Thus, it is established that non-trivial grammatical knowledge has been acquired. Moreover, the parameter configuration of this computational laboratory environment determined using training data generated by the mother language model is validated in a second experiment with a test set similarly resulting in the acquisition of non-trivial categories.

Problem

• How to acquire discrete grammatical categories unsupervised
• Learning abstract grammar from input data patterns
• Validating computational models for language acquisition

Innovation

• Multi-agent system for language learning
• Statistical analysis for grammatical rules
• Hierarchical cluster analysis for categories