Prompt Engineering in Large Language Models

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · see citing works

ChatGPT: five priorities for research

van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023) · Nature · doi:10.1038/d41586-023-00288-7 · Cited by 1,842 · see citing works

MaPLe: Multi-modal Prompt Learning

Khattak, M. U., Rasheed, H., Maaz, M., Khan, S., & Khan, F. S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01832 · Cited by 1,011 · see citing works

Visual Prompt Multi-Modal Tracking

Zhu, J., Lai, S., Chen, X., Wang, D., & Lu, H. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00918 · Cited by 389 · see citing works

Extracting accurate materials data from research papers with conversational language models and prompt engineering

Polak, M. P., & Morgan, D. (2024) · Nature Communications · doi:10.1038/s41467-024-45914-8 · Cited by 365 · see citing works

The CLEAR path: A framework for enhancing information literacy through prompt engineering

Lo, L. S. (2023) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2023.102720 · Cited by 347 · see citing works

The Creativity of Text-to-Image Generation

Oppenlaender, J. (2022) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/3569219.3569352 · Cited by 339 · see citing works

The artificially intelligent entrepreneur: ChatGPT, prompt engineering, and entrepreneurial rhetoric creation

Short, C. E., & Short, J. C. (2023) · Journal of Business Venturing Insights · doi:10.1016/j.jbvi.2023.e00388 · Cited by 235 · see citing works

Prompting for Multi-Modal Tracking

Yang, J., Li, Z., Zheng, F., Leonardis, A., & Song, J. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547851 · Cited by 188 · see citing works

What does CLIP know about a red circle? Visual prompt engineering for VLMs

Shtedritski, A., Rupprecht, C., & Vedaldi, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01101 · Cited by 142 · see citing works

Iteratively Prompt Pre-trained Language Models for Chain of Thought

Wang, B., Deng, X. B., & Sun, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.174 · Cited by 89 · see citing works

AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning

Wang, Y., Agarwal, S., Mukherjee, S., Liu, X., Gao, J., Awadallah, A. H., & Gao, J. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.388 · Cited by 77 · see citing works

Just Tell Me: Prompt Engineering in Business Process Management

Busch, K., Rochlitzer, A., Sola, D., & Leopold, H. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-34241-7_1 · Cited by 69 · see citing works

Medical domain knowledge in domain-agnostic generative AI

Kather, J. N., Laleh, N. G., Foersch, S., & Truhn, D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00634-5 · Cited by 58 · see citing works

ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmarkPreprint

Chalkidis, I. (2023) · Social Science Research Network · doi:10.2139/ssrn.4385460 · Cited by 57 · see citing works

Adding Chit-Chat to Enhance Task-Oriented Dialogues

Sun, K., Moon, S., Crook, P., Roller, S., Silvert, B., Liu, B., Wang, Z., Liu, H., Cho, E., & Cardie, C. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.124 · Cited by 55 · see citing works

On Codex Prompt Engineering for OCL Generation: An Empirical Study

Abukhalaf, S., Hamdaqa, M., & Khomh, F. (2023) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr59073.2023.00033 · Cited by 43 · see citing works

Prompt-Based Meta-Learning For Few-shot Text Classification

Zhang, H., Zhang, X., Huang, H., & Yu, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.87 · Cited by 40 · see citing works

Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in Geotechnical Engineering

Kumar, K. (2023) · Journal of Geotechnical and Geoenvironmental Engineering · doi:10.1061/jggefk.gteng-11828 · Cited by 38 · see citing works

CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction

Lin, J., Chen, Q., Zhou, J., Jin, J., & He, L. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/589 · Cited by 26 · see citing works

ChatGPT4PCG Competition: Character-like Level Generation for Science Birds

Taveekitworachai, P., Abdullah, F., Dewantoro, M. F., Thawonmas, R., Togelius, J., & Renz, J. (2023) · IEEE Conference on Games (CoG) · doi:10.1109/cog57401.2023.10333206 · Cited by 25 · see citing works

MEAL: Stable and Active Learning for Few-Shot Prompting

Köksal, A., Schick, T., & Schuetze, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.36 · Cited by 14 · see citing works

Artificial Intelligence in Music, Sound, Art and Design

Johnson, C. G., Rodríguez-Fernández, N., & Rebelo, S. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-29956-8 · Cited by 8 · see citing works

Large language models for generative information extraction: a survey

Xu, D., Chen, W., Peng, W., Zhang, C., Xu, T., Zhao, X., Wu, X., Zheng, Y., Wang, Y., & Chen, E. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40555-y · Cited by 326 · see citing works

A Survey on Intelligent Network Operations and Performance Optimization Based on Large Language Models

Long, S., Tan, J., Mao, B., Tang, F., Li, Y., Zhao, M., & Kato, N. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3526606 · Cited by 127 · see citing works

Large language models for building energy applications: Opportunities and challenges

Liu, M., Zhang, L., Chen, J., Chen, W.-A., Yang, Z., Lo, L. J., Wen, J., & O’Neill, Z. D. (2025) · Building Simulation · doi:10.1007/s12273-025-1235-9 · Cited by 110 · see citing works

AI vs. human-generated content and accounts on Instagram: User preferences, evaluations, and ethical considerations

Park, J., Oh, C., & Kim, H. Y. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102705 · Cited by 71 · see citing works

Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review

Chen, D., Alnassar, S. A., Avison, K. E., Huang, R. S., & Raman, S. (2025) · JMIR Cancer · doi:10.2196/65984 · Cited by 66 · see citing works

A guide to prompt design: foundations and applications for healthcare simulationists

Maaz, S., Palaganas, J. C., Palaganas, G., & Bajwa, M. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2024.1504532 · Cited by 61 · see citing works

From Programming to Prompting: Developing Computational Thinking through Large Language Model-Based Generative Artificial Intelligence

Hsu, H.-P. (2025) · TechTrends · doi:10.1007/s11528-025-01052-6 · Cited by 57 · see citing works

The impact of artificial intelligence on research efficiency

Madanchian, M., & Taherdoost, H. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104743 · Cited by 79 · see citing works

ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development

Barany, A., Nasiar, N., Porter, C., Zambrano, A. F., Andres, J. M. A. L., Bright, D., Shah, M., Liu, X., Gao, S., Zhang, J., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64299-9_10 · Cited by 51 · see citing works

Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI + era

Wang, W. Y., Zhang, S., Li, G., Lu, J., Ren, Y. S., Wang, X., Gao, X., Su, Y., Song, H., & Li, J. (2024) · Materials Genome Engineering Advances · doi:10.1002/mgea.56 · Cited by 51 · see citing works

Text2BIM: Generating Building Models Using a Large Language Model-Based Multiagent Framework

Du, C., Esser, S., Nousias, S., & Borrmann, A. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6386 · Cited by 72 · see citing works

Can Multimodal Large Language Models Enhance Performance Benefits Among Higher Education Students? An Investigation Based on the Task–Technology Fit Theory and the Artificial Intelligence Device Use Acceptance Model

Al-Dokhny, A. A., Alismaiel, O. A., Youssif, S., Nasr, N., Drwish, A. M., & Samir, A. (2024) · Sustainability · doi:10.3390/su162310780 · Cited by 50 · see citing works

What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use

Ma, Q., Peng, W., Yang, C., Shen, H., Koedinger, K. R., & Wu, T. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3731756 · Cited by 51 · see citing works

Exploring the potential of large language models for improving digital forensic investigation efficiency

Wickramasekara, A., Breitinger, F., & Scanlon, M. (2025) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2024.301859 · Cited by 48 · see citing works

Large language models for automated scholarly paper review: A survey

Zhuang, Z., Chen, J., Xu, H., Jiang, Y., & Lin, J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103332 · Cited by 34 · see citing works

Optimizing text-to-SQL conversion techniques through the integration of intelligent agents and large language models

Ojuri, S., Han, T. A., Chiong, R., & Stefano, A. D. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104136 · Cited by 33 · see citing works

Failure Management Overview in Optical Networks

Cruzes, S. (2024) · IEEE Access · doi:10.1109/access.2024.3498704 · Cited by 38 · see citing works

Large Language Models: Tools for New Environmental Decision-Making

Nie, Q., & Liu, T. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.124373 · Cited by 34 · see citing works

Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests

Deljouyi, A., Koohestani, R., Izadi, M., & Zaidman, A. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00032 · Cited by 28 · see citing works

The Impact of Generative AI on Creativity in Software Development: A Research Agenda

Jackson, V., Vasilescu, B., Russo, D., Ralph, P., Prikladnicki, R., Izadi, M., D’Angelo, S., Inman, S., Andrade, A., & van der Hoek, A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708523 · Cited by 23 · see citing works

Conceptualizing and Operationalizing Prompt Literacy for English Language Learners

Tour, E., & Zadorozhnyy, A. (2025) · Journal of Adolescent & Adult Literacy · doi:10.1002/jaal.70020 · Cited by 35 · see citing works

Opportunities and Challenges of Generative-AI in Finance

Desai, A. P., Ravi, T., Luqman, M., Mallya, G., Kota, N., & Yadav, P. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825658 · Cited by 29 · see citing works

Towards Standardized benchmarks of LLMs in software modeling tasks: a conceptual framework

Cámara, J., Burgueño, L., & Troya, J. (2024) · Software & Systems Modeling · doi:10.1007/s10270-024-01206-9 · Cited by 21 · see citing works

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data AnalysisPreprint

Rasheed, Z., Waseem, M., Ahmad, A., Kemell, K., Wang, X., NguyenDuc, A., & Abrahamsson, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5149683 · Cited by 20 · see citing works

Coal Mine Accident Risk Analysis with Large Language Models and Bayesian Networks

Du, G., & Chen, A. (2025) · Sustainability · doi:10.3390/su17051896 · Cited by 23 · see citing works

Mastering building management systems data points tagging with minimal examples: unveiling the power of large language models

Zheng, Z., Marié, S., Farazdaghi, E., Yahia, E., Makhoul, K., Lagarde, T., Meouche, R. E., & Ababsa, F. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115173 · Cited by 18 · see citing works

From analogy to innovation: A creative conceptual design approach leveraging large language models

Wang, B., Zhao, X., Zuo, H., Song, Y., Han, J., Childs, P., & Chen, L. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103427 · Cited by 18 · see citing works

Harnessing Large Language Models for Structured Reporting in Breast Ultrasound: A Comparative Study of Open AI (GPT-4.0) and Microsoft Bing (GPT-4)

Liu, C., Wei, M., Qin, Y., Zhang, M., Jiang, H., Xu, J., Zhang, Y., Hua, Q., Hou, Y., Dong, Y., et al. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.07.007 · Cited by 18 · see citing works

Simplifying software compliance: AI technologies in drafting technical documentation for the AI Act

Sovrano, F., Hine, E., Anzolut, S., & Bacchelli, A. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10645-x · Cited by 18 · see citing works

Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

Dehkordi, M. K. H., Perl, Y., Deek, F. P., He, Z., Keloth, V. K., Liu, H., Elhanan, G., & Einstein, A. J. (2025) · JMIR Medical Informatics · doi:10.2196/66476 · Cited by 16 · see citing works

Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap

Huang, K., Wang, F., Huang, Y., & Arora, C. (2025) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew66121.2025.00081 · Cited by 17 · see citing works

Responsible AI in Construction Safety: Systematic Evaluation of Large Language Models and Prompt Engineering

Sammour, F., Xu, J., Wang, X., Hu, M., & Zhang, Z. (2025) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-16639 · Cited by 26 · see citing works

Exploring the Integration of Generative AI Tools in Software Testing Education: A Case Study on ChatGPT and Copilot for Preparatory Testing Artifacts in Postgraduate Learning

Haldar, S., Pierce, M., & Fernando Capretz, L. (2025) · IEEE Access · doi:10.1109/access.2025.3545882 · Cited by 16 · see citing works

Reducing hallucinations of large language models via hierarchical semantic piece

Liu, Y., Yang, Q., Tang, J.-W., Guo, T., Chen, W., Pan, L., Sai, X., Gao, X., Li, Z., Liu, J., et al. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01833-9 · Cited by 20 · see citing works

Advancements in feature selection and extraction methods for text mining: a review

Ayash, L., Algarni, A., & Alqahtani, O. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07587-w · Cited by 22 · see citing works

A Comprehensive Survey on LLM-Powered Recommender Systems: From Discriminative, Generative to Multi-Modal Paradigms

Nawara, D., & Kashef, R. F. (2025) · IEEE Access · doi:10.1109/access.2025.3599832 · Cited by 18 · see citing works

Enhancing Complex Linguistic Tasks Resolution Through Fine-Tuning LLMs, RAG and Knowledge Graphs (Short Paper)

Bianchini, F., Calamo, M., Luzi, F. D., Macrì, M., & Mecella, M. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-61003-5_13 · Cited by 16 · see citing works

Chatbots With Attitude: Enhancing Chatbot Interactions Through Dynamic Personality Infusion

Kovačević, N., Boschung, T., Holz, C., Groß, M. H., & Wampfler, R. (2024) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3640794.3665543 · Cited by 17 · see citing works

Automating Software Documentation: Employing LLMs for Precise Use Case Description

Naimi, L., Bouziane, E. M., Jakimi, A., Saadane, R., & Chehri, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.568 · Cited by 17 · see citing works

Assisting early-stage software startups with LLMs: Effective prompt engineering and system instruction design

Ahlgren, T. L., Sunde, H. F., Kemell, K., & Nguyen‐Duc, A. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107832 · Cited by 15 · see citing works

Systematic Prompt Framework for Qualitative Data Analysis: Designing System and User Prompts

Adeseye, A., Isoaho, J., & Tahir, M. (2025) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms65439.2025.11154183 · Cited by 15 · see citing works

A Review of Prompt Engineering Techniques for Large Language Models

Naser, M. Z. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2607553 · Cited by 17 · see citing works

A rapidly structured aircraft concept design method based on generative artificial intelligence

Yao, T. O. N. G., Mingqiang, L. U. O., Shangqing, R. E. N., Zhang, Z., Chenguang, X. I. N. G., & Du, Z. (2025) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2025.103629 · Cited by 14 · see citing works

Rapid generation method of process routes based on multi-agent collaboration with LLMs

Xie, Y., Liu, J., Wang, R., Wang, Z., Yu, K., & Song, Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103733 · Cited by 14 · see citing works

Multilingual Domain Adaptation for Speech Recognition Using LLMs

Ulu, E. N., Derya, E., Tümer, D., Demirel, B., & Karamanlıoğlu, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02548-7_32 · Cited by 14 · see citing works

On the Effectiveness of Large Language Models in Statement-level Code Summarization

Zhu, J., Miao, Y., Xu, T., Zhu, J., & Sun, X. (2024) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs62785.2024.00030 · Cited by 14 · see citing works

SAMBA: A reference framework for Human-in-the-Loop in adaptive Smart Manufacturing

Bianchini, F., Calamo, M., Luzi, F. D., Macrì, M., Marinacci, M., Mathew, J. G., Monti, F., Rossi, J., Leotta, F., & Mecella, M. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.01.286 · Cited by 14 · see citing works

Key Techniques for Writing Effective Prompts

Geroimenko, V. (2025) · SpringerBriefs in computer science · doi:10.1007/978-3-031-86206-9_3 · Cited by 14 · see citing works

Key Principles of Good Prompt Design

Geroimenko, V. (2025) · SpringerBriefs in computer science · doi:10.1007/978-3-031-86206-9_2 · Cited by 14 · see citing works

Prompt engineering in ChatGPT for literature review: practical guide exemplified with studies on white phosphors

Lee, Y., Oh, J. H., Lee, D., Kang, M., & Lee, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-99423-9 · Cited by 14 · see citing works

FinFlier: Automating Graphical Overlays for Financial Visualizations With Knowledge-Grounding Large Language Model

Hao, J., Yang, M., Shi, Q., Jiang, Y., Zhang, G., & Zeng, W. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3514138 · Cited by 13 · see citing works

Large Language Models (LLMs) as Graphing Tools for Advanced Chemistry Education and Research

Subasinghe, S. M. S., Gersib, S. G., & Mankad, N. P. (2025) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c01498 · Cited by 15 · see citing works

Navigating Embodied Intelligence: Enabling Technologies, Security and Privacy, and Emerging Trends

Wang, Y., Niu, X., Ba, J., Su, Z., & Du, L. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3649049 · Cited by 11 · see citing works

The development and evaluation of agricultural question-answering systems based on large language models

Eldem, A., & Eldem, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-35003-9 · Cited by 12 · see citing works

Prompt Engineering for Applied Linguistics: Elements, Examples, Techniques, and Strategies

Lin, Z. (2024) · English Language Teaching · doi:10.5539/elt.v17n9p14 · Cited by 14 · see citing works

Large language model doctor: assessing the ability of ChatGPT-4 to deliver interventional radiology procedural information to patients during the consent process

Hofmann, H. L., & Vairavamurthy, J. P. (2024) · CVIR Endovascular · doi:10.1186/s42155-024-00477-z · Cited by 11 · see citing works

Large Language Models for In-File Vulnerability Localization Can Be “Lost in the End”

Sovrano, F., Bauer, A., & Bacchelli, A. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3715758 · Cited by 12 · see citing works

Code Clone Detection Techniques Based on Large Language Models

Almatrafi, A. A., Eassa, F. E., & Sharaf, S. A. (2025) · IEEE Access · doi:10.1109/access.2025.3549780 · Cited by 11 · see citing works

A black-box assessment of authentication and reliability in consumer IoT devices

Lazzaro, S., Angelis, V. D., Mandalari, A. M., & Buccafurri, F. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102045 · Cited by 11 · see citing works

Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin

Fortuna, C., Hanžel, V., & Bertalanič, B. (2024) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm60555.2024.10738062 · Cited by 10 · see citing works

Harnessing the power of AI-instructor collaborative grading approach: Topic-based effective grading for semi open-ended multipart questions

Myint, P. Y. W., Lo, S. L., & Zhang, Y. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100339 · Cited by 13 · see citing works

Beyond the surface: stylometric analysis of GPT-4o's capacity for literary style imitation

Mikros, G. (2025) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqaf035 · Cited by 23 · see citing works

Social Conflicts in South Korean Public Construction Projects: Insights Using a Large Language Model

Namgoong, D., Baek, S., Won, J. W., & Han, S. H. (2025) · Journal of Management in Engineering · doi:10.1061/jmenea.meeng-6705 · Cited by 10 · see citing works

GenAI-Powered Text Personalization: Natural Language Processing Validation of Adaptation Capabilities

Huynh, L., & McNamara, D. S. (2025) · Applied Sciences · doi:10.3390/app15126791 · Cited by 9 · see citing works

LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems

Duvvuru, V. S. A., Zhang, B., Vierhauser, M., & Agrawal, A. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00223 · Cited by 10 · see citing works

A Glossary of Terms in Artificial Intelligence for Healthcare

Rana, S., Ghahremani, J. S., Woo, J. J., Navarro, R. A., & Ramkumar, P. N. (2024) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2024.08.010 · Cited by 11 · see citing works

How Large Language Models Empower the Analysis of Online Public Engagement for Mega Infrastructure Projects: Cases in Hong Kong

Wang, M. H., Ma, R., Shen, Q., & Xue, J. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3553595 · Cited by 9 · see citing works

Human vs. AI in creative contests: a study on the effectiveness of advertisement copy generated by Chinese large language models

Sun, L., An, Y.-H., Song, X., & Liu, J. (2025) · Electronic Commerce Research · doi:10.1007/s10660-025-09990-2 · Cited by 9 · see citing works

KERMIT: Knowledge graph completion of enhanced relation modeling with inverse transformation

Li, H., Yu, B., Wei, Y., Wang, K., Xu, R. Y. D., & Wang, B. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113500 · Cited by 13 · see citing works

Transphobia Is in the Eye of the Prompter: Trans-Centered Perspectives on Large Language Models

Scheuerman, M. K., Weathington, K., Petterson, A., Doyle, D. T., Das, D., DeVito, M. A., & Brubaker, J. R. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3743676 · Cited by 10 · see citing works

Revolutionizing optical networks: The integration and impact of large language models

Cruzes, S. (2025) · Optical Switching and Networking · doi:10.1016/j.osn.2025.100812 · Cited by 9 · see citing works

DialogueMLLM: Transforming Multimodal Emotion Recognition in Conversation Through Instruction-Tuned MLLM

Sun, Y., & Zhou, T. (2025) · IEEE Access · doi:10.1109/access.2025.3591447 · Cited by 12 · see citing works

Switching Perspectives: Enhancing Generative Artificial Intelligence's Creativity, by Humans, With Design

Bordas, A., Masson, P. L., & Weil, B. (2025) · Creativity and Innovation Management · doi:10.1111/caim.70013 · Cited by 9 · see citing works

The Transformer Led Policing model: a framework for applying generative artificial intelligence in policing

Halford, E. (2025) · Policing A Journal of Policy and Practice · doi:10.1093/police/paaf027 · Cited by 11 · see citing works

Prompt Engineering for Large Language Model-Assisted Inductive Thematic Analysis

Khalid, M. T., & Witmer, A. (2025) · Social Science Computer Review · doi:10.1177/08944393251388098 · Cited by 13 · see citing works

Losing Our Voice? Generative AI and the Degradation of Human Expression

Robbins, S., & Blundell, I. (2025) · Minds and Machines · doi:10.1007/s11023-025-09757-6 · Cited by 9 · see citing works

Enhancing Sentiment Analysis and Error Prediction in Lawyer Questioning Through Legal NLP Models: A Mixed-Methods Study in Australian Courtrooms

Yi, R. (2026) · Journal of Mixed Methods Research · doi:10.1177/15586898261429189 · Cited by 11 · see citing works

Building Bonds Through Bytes: The Impact of Communication Styles on Patient-Chatbot Relationships and Treatment Adherence in AI-Driven Healthcare

Chen, Z., Zhao, X., Hua, M., & Xu, J. L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76809-5_3 · Cited by 8 · see citing works

Exploring the Potential of Offline LLMs in Data Science: A Study on Code Generation for Data Analysis

Νικολακόπουλος, Α., Λίτκε, Α., Psychas, A., Veroni, E., & Varvarigou, T. A. (2025) · IEEE Access · doi:10.1109/access.2025.3556973 · Cited by 10 · see citing works

PromptTutor: Effects of an LLM-Based Chatbot on Learning Outcomes and Motivation in Flipped Classrooms

Zhang, Y., Ouh, E. L., Ho, A., Lo, S. L., Tan, K. W., & Lin, F. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3724363.3729095 · Cited by 9 · see citing works

A-MESS: Anchor-based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition

Shen, Y., Lin, X., & Fan, W. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209824 · Cited by 8 · see citing works

To Ensemble or Not: Assessing Majority Voting Strategies for Phishing Detection with Large Language Models

Trad, F. S., & Chehab, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-82150-9_13 · Cited by 9 · see citing works

DevSec-GPT — Generative-AI (with Custom-Trained Meta's Llama2 LLM), Blockchain, NFT and PBOM Enabled Cloud Native Container Vulnerability Management and Pipeline Verification Platform

Bandara, E., Shetty, S. S., Mukkamala, R., Rahman, A., Foytik, P. B., Liang, X., Zoysa, K. D., & Ng, W. K. (2024) · IEEE Cloud Summit · doi:10.1109/cloud-summit61220.2024.00012 · Cited by 8 · see citing works

ChatGPT’s Expanding Horizons and Transformative Impact Across Domains: A Critical Review of Capabilities, Challenges, and Future Directions

Feyijimi, T. R., Aliu, J., Oke, A. E., & Aghimien, D. O. (2025) · Computers · doi:10.3390/computers14090366 · Cited by 8 · see citing works

AI Applications in Medical Reporting and Diagnosis

Makram, M., & Mohammcd, A. (2024) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc62295.2024.10783552 · Cited by 7 · see citing works

iLLM-TSC: Integration Reinforcement Learning and Large Language Model for Traffic Signal Control Policy Improvement

Pang, A., Wang, M., Pun, M.-O., Chen, C. S., & Xiong, X. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3674284 · Cited by 7 · see citing works

ChatGPT vs. Gemini: Comparative Evaluation in Cybersecurity Education with Prompt Engineering Impact

Nguyen, T., & Sayadi, H. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893499 · Cited by 6 · see citing works

Tackling copyright issues in AI image generation through originality estimation and genericization

Chiba-Okabe, H., & Su, W. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-90827-1 · Cited by 6 · see citing works

Tracking menopause: An SDK Data Audit for intimate infrastructures of datafication with ChatGPT4o

Pybus, J., & Mir, M. (2025) · New Media & Society · doi:10.1177/14614448251314401 · Cited by 6 · see citing works

Tailoring Scientific Knowledge: How Generative AI Personalizes Academic Reading Experiences

Kamińska, A. M. (2025) · Publications · doi:10.3390/publications13020018 · Cited by 9 · see citing works

Using Large Language Models to Embed Relational Cues in the Dialogue of Collaborating Digital Twins

Salman, S., & Richards, D. (2025) · Systems · doi:10.3390/systems13050353 · Cited by 6 · see citing works

Utilizing large language models for semantic enrichment of infrastructure condition data: a comparative study of GPT and Llama models

Höltgen, L., Zentgraf, S., Hagedorn, P., & König, M. (2025) · AI in Civil Engineering · doi:10.1007/s43503-025-00055-9 · Cited by 6 · see citing works

Construction of Q&A methods based on knowledge graphs and large language models-improving the accuracy of landscape pest and disease Q&A

Gu, Z., Long, T., Wang, S., Shang, X., Shen, W., Wei, X., & Xu, K. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101094 · Cited by 7 · see citing works

Using ChatGPT to support human resource management in the hotel industry

Stergiou, D. P. (2025) · Worldwide Hospitality and Tourism Themes · doi:10.1108/whatt-03-2025-0088 · Cited by 6 · see citing works

Feedback Formativo Automatizado com LLMs: Desenvolvimento e Análise de um Sistema para Aprendizagem Progressiva em Programação

Silva, F. G., & Aranha, E. H. D. S. (2025) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/sbie.2025.12807 · Cited by 6 · see citing works

Telemetry and Agentic AI: Foundations for Optical Network Automation

Cruzes, S. (2025) · IEEE Access · doi:10.1109/access.2025.3649768 · Cited by 9 · see citing works

Translating Culture-Specific Items in The True Story of Ah Q: A Comparative Evaluation of GPT-4o, KIMI, and Google Translate Under Multimodal Prompts

Wang, Q., Amini, M., Yang, C., & He, Q. (2026) · SAGE Open · doi:10.1177/21582440251412225 · Cited by 6 · see citing works

Transparent Data Preprocessing for Machine Learning

Strasser, S., & Klettke, M. (2024) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/3665939.3665960 · Cited by 6 · see citing works

Can LLMs revolutionize text mining in chemistry? A comparative study with domain-specific tools

Kumari, M., Chauhan, R., & Garg, P. (2025) · Computer Standards & Interfaces · doi:10.1016/j.csi.2025.103997 · Cited by 7 · see citing works

The Current State of Generative AI Prompt Framework Design for Enhancing Utility in Organizational Decision-Making

Sansanee, H., & Kiattisin, S. (2024) · Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) · doi:10.1109/times-icon61890.2024.10630713 · Cited by 6 · see citing works

Prompt Engineering for Conversational AI Systems: A Systematic Review of Techniques and Applications

Viswanathan, P. S. (2025) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit25111276 · Cited by 12 · see citing works

Optimizing interactions: Strategies for prompt engineering in large language models

Premalatha, V., Kumar, A. D., & Parveen, N. (2025) · Edu - Tech Enterprise · doi:10.71459/edutech202524 · Cited by 10 · see citing works

Prompt Engineering

Qamar, U., & Raza, M. S. (2026) · Springer eBooks · doi:10.1007/978-981-95-1378-9_13

NLP and Prompt Engineering for AI Chatbots

Shah, S. J. H. (2025) · Auerbach Publications eBooks · doi:10.1201/9781003485698-19

Speak the Language of AI: Mastering Prompt Engineering for LLMs

Sreedhar, L. S. (2024) · International Conference on Industrial Engineering and Operations Management (IEOM) · doi:10.46254/in04.20240201 · Cited by 1 · see citing works