Artificial Intelligence (AI) tools become essential across industries, distinguishing AI-generated from human-authored text is increasingly challenging. This study assesses the coherence of AI-generated titles and corresponding abstracts in anticipation of rising AI-assisted document production. Our main goal is to examine the correlation between original and AI-generated titles, emphasizing semantic depth and similarity measures, particularly in the context of Large Language Models (LLMs). We argue that LLMs have transformed research focus, dissemination, and citation patterns across five selected knowledge areas: Business Administration and Management (BAM), Computer Science and Information Technology (CS), Engineering and Material Science (EMS), Medicine and Healthcare (MH), and Psychology and Behavioral Sciences (PBS). We collected 15 000 titles and abstracts, narrowing the selection to 2000 through a rigorous multi-stage screening process adhering to our study’s criteria. Result shows that there is insufficient evidence to suggest that LLM outperforms human authors in article title generation or articles from the LLM era demonstrates a marked difference in semantic richness and readability compared to those from the pre-LLM. Instead, it asserts that LLM is a valuable tool and can assist researchers in generating titles. With LLM’s assistance, the researcher ensures that the content is reflective of the finalized abstract and core research themes, potentially increasing the impact and accessibility and readability of the academic work.
H. Zohny, J. McMillan, and M. King, Ethics of generative AI, Journal of Medical Ethics, vol. 49, no. 2, pp. 79–80, 2023.
M. Z. M. Hurmuz, S. M. Jansen-Kosterink, I. Flierman, S. del Signore, G. Zia, S. del Signore, and B. Fard, Are social robots the solution for shortages in rehabilitation care? Assessing the acceptance of nurses and patients of a social robot, Comput. Hum. Behav. Artif. Hum., vol. 1, no. 2, p. 100017, 2023.
Z. Tekic and J. Füller, Managing innovation in the era of AI, Technol. Soc., vol. 73, p. 102254, 2023.
P. M. Mah, I. Skalna, and J. Muzam, Natural language processing and artificial intelligence for enterprise management in the era of industry 4.0, Appl. Sci., vol. 12, no. 18, p. 9207, 2022.
R. Tolosana, S. Romero-Tapiador, R. Vera-Rodriguez, E. Gonzalez-Sosa, and J. Fierrez, DeepFakes detection across generations: Analysis of facial regions, fusion, and performance evaluation, Eng. Appl. Artif. Intell., vol. 110, p. 104673, 2022.
K. Somoray and D. J. Miller, Providing detection strategies to improve human detection of deepfakes: An experimental study, Comput. Hum. Behav., vol. 149, p. 107917, 2023.
N. Serki and S. Bolkan, The effect of clarity on learning: Impacting motivation through cognitive load, Commun. Educ., vol. 73, no. 1, pp. 29–45, 2024.
Z. Elyoseph, D. Hadar-Shoval, K. Asraf, and M. Lvovsky, ChatGPT outperforms humans in emotional awareness evaluations, Front. Psychol., vol. 14, p. 1199058, 2023.
M. S. Tullu, Writing the title and abstract for a research paper: Being concise, precise, and meticulous is the key, Saudi J. Anaesth., vol. 13, no. Suppl1, pp. S12–S17, 2019.
H. H. Thorp, ChatGPT is fun, but not an author, Science, vol. 379, no. 6630, p. 313, 2023.
Q. Zhou, B. Li, L. Han, and M. Jou, Talking to a bot or a wall? How chatbots vs. human agents affect anticipated communication quality, Computers in Human Behavior, vol. 143, p. 107674, 2023.
R. Saptono, H. Prasetyo, and A. Irawan, Combination of cosine similarity method and conditional probability for plagiarism detection in the thesis documents vector space model, Journal of Telecommunication, vol. 10, nos. 2–4, pp. 139–143, 2018.
S. Tata and J. M. Patel, Estimating the selectivity of TF-IDF based cosine similarity predicates, SIGMOD Rec., vol. 36, no. 4, pp. 75–80, 2007.
L. S. Lo, The art and science of prompt engineering: A new literacy in the information age, Internet Ref. Serv. Q., vol. 27, no. 4, pp. 203–210, 2023.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., Language models are few-shot learners, Adv. Neural Inf. Process. Syst., vol. 33, pp. 1877–1901, 2020.
D. Peters, K. Vold, D. Robinson, and R. A. Calvo, Responsible AI—Two frameworks for ethical design practice, IEEE Trans. Technol. Soc., vol. 1, no. 1, pp. 34–47, 2020.
H. Huang, D. Zhu, and X. Wang, Evaluating scientific impact of publications: Combining citation polarity and purpose, Scientometrics, vol. 127, no. 9, pp. 5257–5281, 2022.
B. I. Hutchins, X. Yuan, J. M. Anderson, and G. M. Santangelo, Relative citation ratio (RCR): A new metric that uses citation rates to measure influence at the article level, PLoS Biol., vol. 14, no. 9, p. e1002541, 2016.
M. R. Dougherty and Z. Horne, Citation counts and journal impact factors do not capture some indicators of research quality in the behavioural and brain sciences, R. Soc. Open Sci., vol. 9, no. 8, p. 220334, 2022.
A. P. Akella, H. Alhoori, P. R. Kondamudi, C. Freeman, and H. Zhou, Early indicators of scientific impact: Predicting citations with altmetrics, Journal of Informatics, vol. 15, no. 2, p. 101128, 2021.
G. Le Mens, B. Kovács, M. T. Hannan, and G. Pros, Uncovering the semantics of concepts using GPT-4, Proc. Natl. Acad. Sci. U.S.A., vol. 120, no. 49, p. e2309350120, 2023.