Large Language Models (LLMs) are a type of machine learning that handles a wide range of Natural Language Processing (NLP) scenarios. Recently, in December 2022, a company called OpenAI released ChatGPT, a tool that, within a few months, became the most representative example of LLMs, automatically generating unique and coherent text on many topics, summarising and rewriting it, or even translating it to other languages. ChatGPT originated some controversy in academia since students can generate unique text for writing assessments being sometimes extremely difficult to distinguish whether it comes from ChatGPT or a person. In research, some journals specifically banned ChatGPT in scientific papers. However, when used correctly, it becomes a powerful tool to rewrite, for instance, scientific papers and, thus, deliver researchers’ messages in a better way. In this paper, we conduct an empirical study of the impact of ChatGPT in research. We downloaded the abstract of over 45,000 papers from over 300 journals from Dec 2022 and Feb 2023 belonging to different research editorials. We use four of the most known ChatGPT detection tools and conclude that ChatGPT played a role in around 10% of the papers published in every editorial, showing that authors from different fields have rapidly adopted such a tool in their research.

Large Language Models have become important in our lives, and academia is not agnostic to this trend, offering tools like text rephrasing and summarisation. However, this integration raises significant concerns regarding the integrity of science. In this paper, we investigate hallucinations of LLMs when generating scientific references. Using nine LLMs, we generated a dataset of 74,196 $\mathrm{B}\mathrm{I}\mathrm{B}{\mathrm{T}}_{\mathrm{E}}\mathrm{X}$ references to quantify and analyse fabricated references, focusing on distinguishing between intrinsic and extrinsic hallucinations. Also, we extracted and analysed 127,063 references from 3541 published papers in 2023 to assess the prevalence of fake bibliographic data. Our manual verification process identified eight instances of fabricated references. While the overall rate is statistically low, the mere existence of fabricated content in the peer-reviewed literature is a critical integrity issue, demonstrating a vulnerability in current academic validation systems. The significance of our finding is not the statistical prevalence but rather the necessity for rigorous, human-validated processes to prevent the injection of spurious citations regardless of their source.