Prompt Evolution for Generative AI: A Classifier-Guided Approach

[2305.16347] Prompt Evolution for Generative AI: A Classifier-Guided Approach Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2305.16347 (cs) [Submitted on 24 May 2023 ( v1 ), last revised 14 Apr 2026 (this version, v2)] Title: Prompt Evolution for Generative AI: A Classifier-Guided Approach Authors: Melvin Wong , Yew-Soon Ong , Abhishek Gupta , Kavitesh K. Bali , Caishun Chen View a PDF of the paper titled Prompt Evolution for Generative AI: A Classifier-Guided Approach, by Melvin Wong and 4 other authors View PDF HTML (experimental) Abstract: Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI. However, such models often fail to connect the generated outputs and desired target concepts/preferences implied by the prompts. Current research addressing this limitation has largely focused on enhancing the prompts before output generation or improving the model's performance up front. In contrast, this paper conceptualizes prompt evolution, imparting evolutionary selection pressure and variation during the generative process to produce multiple outputs that satisfy the target concepts/preferences better. We propose a multi-objective instantiation of this broader idea that uses a multi-label image classifier-guided approach. The predicted labels from the classifiers serve as multiple objectives to optimize, with the aim of producing diversified images that meet user preferences. A novelty of our evolutionary algorithm is that the pre-trained generative model gives us implicit mutation operations, leveraging the model's stochastic generative capability to automate the creation of Pareto-optimized images more faithful to user preferences. Comments: This work is published in the Proceedings of the IEEE Conference on Artificial Intelligence (CAI 2023). IEEE copyrights applies Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE) ACM classes: I.2 Cite as: arXiv:2305.16347 [cs.LG] (or arXiv:2305.16347v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2305.16347 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Conference on Artificial Intelligence (CAI) 2023 (Pages 226-229) Related DOI : https://doi.org/10.1109/CAI54212.2023.00105 Focus to learn more DOI(s) linking to related resources Submission history From: Melvin Wong [ view email ] [v1] Wed, 24 May 2023 14:48:18 UTC (11,460 KB) [v2] Tue, 14 Apr 2026 07:08:36 UTC (7,606 KB) Full-text links: Access Paper: View a PDF of the paper titled Prompt Evolution for Generative AI: A Classifier-Guided Approach, by Melvin Wong and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2023-05 Change to browse by: cs cs.AI cs.CV cs.NE References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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