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A master's thesis research project from the University of Siena, which compares the similarities and differences in argument linking abilities between 4-5 year-old children and large language models (LLMs) through experiments on Uzbek psych verbs, exploring the boundaries between language acquisition and machine language understanding.
Section 01
This study is a master's thesis project at the University of Siena, focusing on the argument linking ability of Uzbek psych verbs, comparing the performance of 4-5 year-old native-speaking children and large language models (LLMs) to explore the boundaries between human language acquisition mechanisms and machine language understanding. The research results are open-sourced on GitHub (author: Madina-Kh, release date: 2026-06-01).
Section 02
The argument structure of psych verbs (e.g., "fear", "like") is cross-linguistically complex. As an agglutinative language in the Turkic family, Uzbek's case-marking system provides a unique perspective for argument linking research. This study innovatively compares child language acquisition (critical period: 4-5 years old) with LLM performance to explore their similarities and differences in complex syntactic understanding.
Section 03
The experiment recruited monolingual 4-5 year-old children from Uzbekistan kindergartens (screened for language ability). Animated short films plus newly coined psych verbs were used as stimulus materials, and children were asked to describe scenes to test argument mapping. LLMs were evaluated via zero-shot/few-shot prompts using the same stimuli, and the analysis process was consistent with that of children's data.
Section 04
Children showed the "experiencer-first" principle (tendency to map the experiencer as the subject, with strong rule generalization ability). Although LLMs can generate grammatically correct sentences, they rely on frequency patterns in training data, and their performance drops significantly under low-frequency argument configurations. The differences between the two reflect the essential distinction between human rule-based learning and machine data-driven learning.
Section 05
The study provides interdisciplinary references for linguistics (Turkic psych verb data), psychology (child syntactic development), and AI (LLM syntactic ability evaluation benchmarks). Future directions can expand to more languages, or delve into LLM performance on other syntactic phenomena, to build a more comprehensive machine language ability evaluation framework.
Section 06
The project's GitHub repository contains de-identified child experiment data, R/Python analysis code, experimental animations/instructions, and LLM prompt templates, supporting other researchers to replicate, verify, and extend the study, embodying open science practices.
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