Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans
arXiv:2308.07462 · doi:10.1016/j.mlwa.2024.100602
Abstract
The introduction of Artificial Intelligence (AI) generative language models such as GPT (Generative Pre-trained Transformer) and tools such as ChatGPT has triggered a revolution that can transform how text is generated. This has many implications, for example, as AI-generated text becomes a significant fraction of the text, would this have an effect on the language capabilities of readers and also on the training of newer AI tools? Would it affect the evolution of languages? Focusing on one specific aspect of the language: words; will the use of tools such as ChatGPT increase or reduce the vocabulary used or the lexical richness? This has implications for words, as those not included in AI-generated content will tend to be less and less popular and may eventually be lost. In this work, we perform an initial comparison of the vocabulary and lexical richness of ChatGPT and humans when performing the same tasks. In more detail, two datasets containing the answers to different types of questions answered by ChatGPT and humans, and a third dataset in which ChatGPT paraphrases sentences and questions are used. The analysis shows that ChatGPT tends to use fewer distinct words and lower lexical richness than humans. These results are very preliminary and additional datasets and ChatGPT configurations have to be evaluated to extract more general conclusions. Therefore, further research is needed to understand how the use of ChatGPT and more broadly generative AI tools will affect the vocabulary and lexical richness in different types of text and languages.
References in corpus (24)
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- LoRA: Low-Rank Adaptation of Large Language Models
- Hierarchical Text-Conditional Image Generation with CLIP Latents
- QLoRA: Efficient Finetuning of Quantized LLMs
- No Language Left Behind: Scaling Human-Centered Machine Translation
- How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
- The Curse of Recursion: Training on Generated Data Makes Models Forget
- Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
- Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
- Mapping the Increasing Use of LLMs in Scientific Papers
- A Tale of Tails: Model Collapse as a Change of Scaling Laws
- The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
- Linguistic ambiguity analysis in ChatGPT
- CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts
- Understanding the Impact of Artificial Intelligence in Academic Writing: Metadata to the Rescue
- Text to Image Generation: Leaving no Language Behind
- Better & Faster Large Language Models via Multi-token Prediction
- Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet
- Is ChatGPT Transforming Academics' Writing Style?
- Emergence of a phonological bias in ChatGPT
- "Genlangs" and Zipf's Law: Do languages generated by ChatGPT statistically look human?
- Heat Death of Generative Models in Closed-Loop Learning
- Building another Spanish dictionary, this time with GPT-4