Multimodal Prompt Engineering via Self-Generated Semantic Cues

Zhang et al. (2025) showcased a “self-prompt” system for image outpainting, auto-generating semantic cues in the absence of captions. This idea generalizes and extends that approach: imagine a toolkit where, for any multimodal task (text-to-image, text-to-audio, etc.), the LLM infers and generates contextually rich semantic “prompts” (e.g., tag clouds, visual sketches, sound snippets) to guide downstream generative models. Unlike current workflows requiring detailed manual prompt engineering, this system would let users provide minimal hints, with the model filling in the necessary semantic scaffolding. Research could focus on prompt autoencoder architectures, evaluation of multimodal prompt richness, and the effect on generation quality and user creativity. This opens up scalable, user-friendly interfaces for non-experts, and could be transformative for accessibility, education, and creative industries.

References:

1. Self-Prompt Guided Image Outpainting Model for Captions Absence in Social Scenes. Zongyan Zhang, C. L. P. Chen, Haohan Weng, Tong Zhang (2025). IEEE Transactions on Computational Social Systems.