# Can AI mimic the human ability to define neologisms?

arXiv: https://arxiv.org/abs/2502.14900 · HTML: https://mlsanity.com/paper/2502.14900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Do large language models resemble humans in language use?](https://mlsanity.com/paper/2303.08014/) (2023)
- [Evaluating Morphological Compositional Generalization in Large Language Models](https://mlsanity.com/paper/2410.12656/) (2024)
- [Will it Unblend?](https://mlsanity.com/paper/2009.09123/) (2020)
- [Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model](https://mlsanity.com/paper/2310.15113/) (2023)
- [A Psycholinguistic Analysis of BERT's Representations of Compounds](https://mlsanity.com/paper/2302.07232/) (2023)
- [Differentiating Between Human-Written and AI-Generated Texts Using Automatically Extracted Linguistic Features](https://mlsanity.com/paper/2407.03646/) (2024)

## Built on by (most cited first, 1 of 1)

- [Artificial Intelligence in Pronunciation Teaching: Use and Beliefs of Foreign Language Teachers](https://mlsanity.com/paper/2503.04128/) (2025)

## Abstract (arXiv)

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.