Can AI mimic the human ability to define neologisms?

TL;DR

This study investigates AI's ability to define Greek neologisms, revealing partial success with blends and derivatives but challenges with compounds, emphasizing the need for advanced semantic understanding in AI models.

Contribution

It provides empirical data on AI's performance in defining different types of neologisms, highlighting specific areas for improvement in language modeling.

Findings

Fair agreement between AI and humans for blends and derivatives

No agreement for compounds, but high agreement when considering majority human responses

Highlights the need for improved semantic networks and contextual learning in AI

Abstract

One ongoing debate in linguistics is whether Artificial Intelligence (AI) can effectively mimic human performance in language-related tasks. While much research has focused on various linguistic abilities of AI, little attention has been given to how it defines neologisms formed through different word formation processes. This study addresses this gap by examining the degree of agreement between human and AI-generated responses in defining three types of Greek neologisms: blends, compounds, and derivatives. The study employed an online experiment in which human participants selected the most appropriate definitions for neologisms, while ChatGPT received identical prompts. The results revealed fair agreement between human and AI responses for blends and derivatives but no agreement for compounds. However, when considering the majority response among humans, agreement with AI was high for blends and derivatives. These findings highlight the complexity of human language and the challenges AI still faces in capturing its nuances. In particular, they suggest a need for integrating more advanced semantic networks and contextual learning mechanisms into AI models to improve their interpretation of complex word formations, especially compounds.