References
- Pornprasit, Chanathip, C. Tantithamthavorn. Fine-Tuning and Prompt Engineering for Large Language Models-Based Code Review Automation. – Information and Software Technology, Vol. 175, 2024, 107523.
- Heston, T. F., C. Khun. Prompt Engineering in Medical Education. – International Medical Education, Vol. 2, 2023, No 3, pp. 198-205.
- He, X., S. Zannettou, Y. Shen, Y. Zhang. You only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content. – In: 2024 IEEE Symposium on Security and Privacy (SP’24), 2024, pp. 770-787.
- Sabbatella, A., A. Ponti, I. Giordani, A. Candelieri, F. Archetti. Prompt Optimization in Large Language Models. – Mathematics, Vol. 12, 2024, No 6, p. 929.
- Song, Y. F., Y. Q. He, X. F. Zhao, H. L. Gu, D. Jiang, H. J. Yang, L. X. Fan. A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models. – Journal of Computer Science and Technology, Vol. 39, 2024, No 4, pp. 984-1004.
- Knoth, N., A. Tolzin, A. Janson, J. M. Leimeister. AI Literacy and Its Implications for Prompt Engineering Strategies. – Computers and Education: Artificial Intelligence, Vol. 6, 2024, 100225.
- Liu, S., X. Chen, Qu, K. Tang, Y. S. Ong. Large Language Models as Evolutionary Optimizers. – In: 2024 IEEE Congress on Evolutionary Computation (CEC’24), June 2024, pp. 1-8.
- He, C., Y. Tian, Z. Lu. Artificial Evolutionary Intelligence (AEI): Evolutionary Computation Evolves with Large Language Models. – Journal of Membrane Computing, 2024, pp. 1-18.
- Patania, S., E. Masiero, L. Brini, V. Piskovskyi, D. Ognibene, G. Donabauer, U. Kruschwitz. Large Language Models as an Active Bayesian Filter: Information Acquisition and Integration. – In: Proc. of 28th Workshop on the Semantics and Pragmatics of Dialogue, September 2024.
- Chen, S., W. Wang, X. Chen, M. Zhang, P. Lu, X. Li, Y. Du. Enhancing Chinese Comprehension and Reasoning for Large Language Models: An Efficient LoRA Fine-Tuning and Tree of Thoughts Framework. – Journal of Supercomputing, Vol. 81, 2025, No 1, p. 50.
- Klyuchnikov, N., I. Trofimov, E. Artemova, M. Salnikov, M. Fedorov, A. Filippov, E. Burnaev. Nas-Bench-Nlp: Neural Architecture Search Benchmark for Natural Language Processing. – IEEE Access, Vol. 10, 2022, pp. 45736-45747.
- Zhao, B., W. Jin, Y. Zhang, S. Huang, G. Yang. Prompt Learning for Metonymy Resolution: Enhancing Performance with Internal Prior Knowledge of Pre-Trained Language Models. – Knowledge-Based Systems, Vol. 279, 2023, 110928.
- De Curtò, J., I. de Zarzà, G. Roig, J. C. Cano, P. Manzoni, C. T. Calafate. Llm-Informed Multi-Armed Bandit Strategies for Non-Stationary Environments. – Electronics, Vol. 12, 2023, No 13, 2814.
- Ahmed, A., X. Zeng, R. Xi, M. Hou, S. A. Shah. MED-Prompt: A Novel Prompt Engineering Framework for Medicine Prediction on Free-Text Clinical Notes. – Journal of King Saud University-Computer and Information Sciences, Vol. 36, 2024, No 2, 101933.
- Liu, S., C. Chen, X. Qu, K. Tang, Y. S. Ong. Large Language Models as Evolutionary Optimizers. – In: Proc. of IEEE Congress on Evolutionary Computation (CEC’24), June 2024, pp. 1-8.
- Sorokin, L., D. Safin, Sh. Nejati. Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs? – Empirical Software Engineering, Vol. 30, 2025, No 1, pp. 1-39.
- Kumar, S., D. Deepika, K. Slater, V. Kumar. AOPWIKI-EXPLORER: An Interactive Graph-Based Query Engine Leveraging Large Language Models. – Computational Toxicology, Vol. 30, 2024, 100308.
- Qiu, Y., Y. Jin. ChatGPT and Finetuned BERT: A Comparative Study for Developing Intelligent Design Support Systems. – Intelligent Systems with Applications, Vol. 21, 2024, 200308.
- GLUE Dataset. https://www.kaggle.com/datasets/thedevastator/nli-dataset-for-sentence-understanding
- Soni, U., D. A. G. Gordhan Jethava, A. Ganatra. Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques. – Cybernetics and Information Technologies, Vol. 24, 2024, No 4, pp. 22-44.
- Ngo, V. B., V. H. Vu. Multi-Level Machine Learning Model to Improve the Effectiveness of Predicting Customer Churn in Banks. – Cybernetics and Information Technologies, Vol. 24, 2024, No 3, pp. 3-20.
- Vincent, A. M., P. Jidesh. An Improved Hyperparameter Optimization Framework for AutoML Systems Using Evolutionary Algorithms. – Scientific Reports, Vol. 13, 2023, No 1, 4737.
- Bakır, H., Ö. Ceviz. Empirical Enhancement of Intrusion Detection Systems: A Comprehensive Approach with Genetic Algorithm-Based Hyperparameter Tuning and Hybrid Feature Selection. – Arabian Journal for Science and Engineering, Vol. 49, 2024, No 9, pp. 13025-13043.
- Al Saba, M. T., N. A. Hakami, K. S. AlJebreen, M. A. Abido. Multi-Objective Distributionally Robust Approach for Optimal Location of Renewable Energy Sources. – Alexandria Engineering Journal, Vol. 77, 2023, pp. 75-94.
- Harane, P. P., D. R. Unune, R. Ahmed, S. Wojciechowski. Multi-Objective Optimization for Electric Discharge Drilling of Waspaloy: A Comparative Analysis of NSGA-II, MOGA, MOGWO, and MOPSO. – Alexandria Engineering Journal, Vol. 99, 2024, pp. 1-16.
- GSM8K Dataset Link. https://www.kaggle.com/datasets/thedevastator/grade-school-math-8k-q-a
