# Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study

arXiv: https://arxiv.org/abs/2402.15518 · HTML: https://mlsanity.com/paper/2402.15518/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 12 of 12)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [On the Stability of Iterative Retraining of Generative Models on their own Data](https://mlsanity.com/paper/2310.00429/) (2023)
- [Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop](https://mlsanity.com/paper/2311.16822/) (2023)
- [Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet](https://mlsanity.com/paper/2306.06130/) (2023)
- [Emergence of a phonological bias in ChatGPT](https://mlsanity.com/paper/2305.15929/) (2023)
- [Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie](https://mlsanity.com/paper/2309.09120/) (2023)

## Built on by (most cited first, 10 of 10)

- [The Homogenizing Effect of Large Language Models on Human Expression and Thought](https://mlsanity.com/paper/2508.01491/) (2025)
- [DoPAMine: Domain-specific Pre-training Adaptation from seed-guided data Mining](https://mlsanity.com/paper/2410.00260/) (2024)
- [What if AI systems weren't chatbots?](https://mlsanity.com/paper/2605.07896/) (2026)
- [Does a Large Language Model Really Speak in Human-Like Language?](https://mlsanity.com/paper/2501.01273/) (2025)
- [Do LLMs produce texts with "human-like" lexical diversity?](https://mlsanity.com/paper/2508.00086/) (2025)
- [AI Brown and AI Koditex: LLM-Generated Corpora Comparable to Traditional Corpora of English and Czech Texts](https://mlsanity.com/paper/2509.22996/) (2025)
- [Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)](https://mlsanity.com/paper/2605.27268/) (2026)
- [Quantifying the Risks of Tool-assisted Rephrasing to Linguistic Diversity](https://mlsanity.com/paper/2410.17670/) (2024)
- [A Deep User Interface for Exploring LLaMa](https://mlsanity.com/paper/2502.20938/) (2025)
- [IDEAlign: Comparing Ideas of Large Language Models to Domain Expert](https://mlsanity.com/paper/2509.02855/) (2025)

## Abstract (arXiv)

The performance of conversational Large Language Models (LLMs) in general, and of ChatGPT in particular, is currently being evaluated on many different tasks, from logical reasoning or maths to answering questions on a myriad of topics. Instead, much less attention is being devoted to the study of the linguistic features of the texts generated by these LLMs. This is surprising since LLMs are models for language, and understanding how they use the language is important. Indeed, conversational LLMs are poised to have a significant impact on the evolution of languages as they may eventually dominate the creation of new text. This means that for example, if conversational LLMs do not use a word it may become less and less frequent and eventually stop being used altogether. Therefore, evaluating the linguistic features of the text they produce and how those depend on the model parameters is the first step toward understanding the potential impact of conversational LLMs on the evolution of languages. In this paper, we consider the evaluation of the lexical richness of the text generated by LLMs and how it depends on the model parameters. A methodology is presented and used to conduct a comprehensive evaluation of lexical richness using ChatGPT as a case study. The results show how lexical richness depends on the version of ChatGPT and some of its parameters, such as the presence penalty, or on the role assigned to the model. The dataset and tools used in our analysis are released under open licenses with the goal of drawing the much-needed attention to the evaluation of the linguistic features of LLM-generated text.