Prompt engineering is effective for controlling the output of text-to-image
(T2I) generative models, but it is also laborious due to the need for manually
crafted prompts. This challenge has spurred the development of algorithms for
automated prompt generation. However, these methods often struggle with
transferability across T2I models, require white-box access to the underlying
model, and produce non-intuitive prompts. In this work, we introduce PRISM, an
algorithm that automatically identifies human-interpretable and transferable
prompts that can effectively generate desired concepts given only black-box
access to T2I models. Inspired by large language model (LLM) jailbreaking,
PRISM leverages the in-context learning ability of LLMs to iteratively refine
the candidate prompts distribution for given reference images. Our experiments
demonstrate the versatility and effectiveness of PRISM in generating accurate
prompts for objects, styles and images across multiple T2I models, including
Stable Diffusion, DALL-E, and Midjourney.

PRISM 是一种自动识别人类可解释且可迁移的提示的算法，它可以有效地生成所需概念，仅通过对 T2I 模型的黑盒访问。

个性化文本到图像生成的自动黑盒提示工程

Automated Black-box Prompt Engineering for Personalized Text-to-Image  Generation

Recent advancements in generative AI have enabled ubiquitous access to large
language models (LLMs). Empowered by their exceptional capabilities to
understand and generate human-like text, these models are being increasingly
integrated into our society. At the same time, there are also concerns on the
potential misuse of this powerful technology, prompting defensive measures from
service providers. To overcome such protection, jailbreaking prompts have
recently emerged as one of the most effective mechanisms to circumvent security
restrictions and elicit harmful content originally designed to be prohibited.
Due to the rapid development of LLMs and their ease of access via natural
languages, the frontline of jailbreak prompts is largely seen in online forums
and among hobbyists. To gain a better understanding of the threat landscape of
semantically meaningful jailbreak prompts, we systemized existing prompts and
measured their jailbreak effectiveness empirically. Further, we conducted a
user study involving 92 participants with diverse backgrounds to unveil the
process of manually creating jailbreak prompts. We observed that users often
succeeded in jailbreak prompts generation regardless of their expertise in
LLMs. Building on the insights from the user study, we also developed a system
using AI as the assistant to automate the process of jailbreak prompt
generation.

通过系统化研究和用户调查，我们发现生成式 AI 技术中的大型语言模型 (LLMs) 存在滥用、绕过安全限制的风险，尤其是使用 jailbreak prompts 进行潜在有害内容的生成，为了应对这一问题，我们开发了一个基于人工智能的系统来自动化产生 jailbreak prompts 的过程。

不要听我说：理解和探索大型语言模型的越狱提示

Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of  Large Language Models

Well-designed prompts have demonstrated the potential to guide text-to-image
models in generating amazing images. Although existing prompt engineering
methods can provide high-level guidance, it is challenging for novice users to
achieve the desired results by manually entering prompts due to a discrepancy
between novice-user-input prompts and the model-preferred prompts. To bridge
the distribution gap between user input behavior and model training datasets,
we first construct a novel Coarse-Fine Granularity Prompts dataset (CFP) and
propose a novel User-Friendly Fine-Grained Text Generation framework (UF-FGTG)
for automated prompt optimization. For CFP, we construct a novel dataset for
text-to-image tasks that combines coarse and fine-grained prompts to facilitate
the development of automated prompt generation methods. For UF-FGTG, we propose
a novel framework that automatically translates user-input prompts into
model-preferred prompts. Specifically, we propose a prompt refiner that
continually rewrites prompts to empower users to select results that align with
their unique needs. Meanwhile, we integrate image-related loss functions from
the text-to-image model into the training process of text generation to
generate model-preferred prompts. Additionally, we propose an adaptive feature
extraction module to ensure diversity in the generated results. Experiments
demonstrate that our approach is capable of generating more visually appealing
and diverse images than previous state-of-the-art methods, achieving an average
improvement of 5% across six quality and aesthetic metrics.

通过构建 Coarse-Fine Granularity Prompts 数据集和 User-Friendly Fine-Grained Text Generation 框架，该研究提出了一种自动化提示优化方法，能够以更高的准确度生成视觉吸引力和多样性更强的图像。