The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Prompt Engineering in Large Language Models
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Current GenAI technologies in use in software development firmly have the user in charge: through prompt engineering [82], the software professional steers, and thus controls the outcomes.”
From The Impact of Generative AI on Creativity in Software Development: A Research Agenda (2025) · cites this paper as [82]
In the citing paper
“In comparison, prompt engineering enables the creation of more effective queries that align closely with the specific information needs of specialty fields such as oncology, steering LLMs toward more precise data retrieval [48].”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [48]
Citations
Cites
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352
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
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 1,125
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 485
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 404
The Creativity of Text-to-Image Generation
Oppenlaender, J. (2022) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/3569219.3569352 · Cited by 401
Polak, M. P., & Morgan, D. (2024) · Nature Communications · doi:10.1038/s41467-024-45914-8 · Cited by 365
Short, C. E., & Short, J. C. (2023) · Journal of Business Venturing Insights · doi:10.1016/j.jbvi.2023.e00388 · Cited by 273
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 199
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 191
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 123
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
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 76
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
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
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
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 53
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 50
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 45
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 27
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
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 19
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
Research works printed but not linked: 15
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- Wei J et al (2022) Emergent abilities of large language models. arXiv cs.CL
- White J et al (2023) A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv cs.SE
- Strobelt H et al (2023) Interactive and visual prompt engineering for ad-hoc task adaptation with large language models. IEEE Trans Vis Comput Graph 29(1):1146–1156
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- Trautmann D, Petrova A, Schilder F (2022) Legal prompt engineering for multilingual legal judgement prediction. arXiv cs.CL
- Ahmed T, Pai KS, Devanbu P, Barr ET (2023) Improving few-shot prompts with relevant static analysis products. arXiv cs.SE
- Diao S, Wang P, Lin Y, Zhang T (2023) Active prompting with chain-of-thought for large language models. arXiv cs.CL
- Yang Z et al (2023) MM-REACT: prompting ChatGPT for multimodal reasoning and action. arXiv cs.CV
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- Wang Z, Panda R, Karlinsky L, Feris R, Sun H, Kim Y (2023) Multitask prompt tuning enables parameter-efficient transfer learning. arXiv cs.CL
- Kojima T, Gu SS, Reid MM, Matsuo Y, Iwasawa Y (2022) Large language models are zero-shot reasoners. arXiv cs.CL
- Zhang T, Wang X, Zhou D, Schuurmans D, Gonzalez JE (2022) TEMPERA: test-time prompting via Reinforcement learning. arXiv cs.CL
- Zhou Y et al (2022) Large language models are human-level prompt engineers. arXiv cs.LG
- Austin J et al (2021) Program synthesis with large language models. arXiv cs.PL
Cited by
Sorted by citations
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
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 139
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 116
Park, J., Oh, C., & Kim, H. Y. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102705 · Cited by 86
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
Metadata
- Authors
- Marvin, G., Hellen, N., Jjingo, D., & Nakatumba-Nabende, J.
- Affiliations
- Makerere University (UG)
- Muni University (UG)
- Corresponding author
- Marvin, Ggaliwango
- Venue
- Algorithms for Intelligent Systems
- Publisher
- Springer Nature Singapore
- Published
- 2024
- Pages
- 387-402
- ISSN
- 2524-7565 (ISSN-L)
- 2524-7573
- OpenAlex
- W4390638022
- Cited by
- 403
- References
- 23
- Citations by year
- 2026: 112 · 2025: 227 · 2024: 64
- FWCI
- 119.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Algorithms for intelligent systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- prompt engineering, large language models, instruction selection, conversational AI, ChatGPT, process automation, responsible AI, prompt optimization
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