Taxonomy Exploration with Large Language Model Reasoning and Tool Calling

Abstract

The paper addresses the task of taxonomy expansion – hierarchical structures for organizing concepts. We propose an architecture based on the ReAct (Reasoning + Acting) approach that enables taxonomy expansion in a zero-shot setting without fine-tuning large language models. The system is implemented in two scenarios: autonomous navigation from root nodes and verification of hypotheses generated by other models. Experiments on the diachronic RuWordNet dataset show that direct navigation from the root faces limitations due to graph complexity (MAP@3 = 24.6%). However, using the system as a verifier improves the performance of baseline models: MAP@3 gains of 9.5 pp for FastText and 1.1 pp for TaxoYandexGPT-5-Lite. The key advantages of the approach are its universality, the absence of fine-tuning requirements, and interpretability through explicit reasoning chains.