In this past year, large language models (LLMs) have had remarkable success
in domains outside the traditional natural language processing, and people are
starting to explore the usage of LLMs in more general and close to application
domains like code generation, travel planning, and robot controls. Connecting
these LLMs with great capacity and external tools, people are building the
so-called LLM agents, which are supposed to help people do all kinds of work in
everyday life. In all these domains, the prompt to the LLMs has been shown to
make a big difference in what the LLM would generate and thus affect the
performance of the LLM agents. Therefore, automatic prompt engineering has
become an important question for many researchers and users of LLMs. In this
paper, we propose a novel method, \textsc{RePrompt}, which does "gradient
descent" to optimize the step-by-step instructions in the prompt of the LLM
agents based on the chat history obtained from interactions with LLM agents. By
optimizing the prompt, the LLM will learn how to plan in specific domains. We
have used experiments in PDDL generation and travel planning to show that our
method could generally improve the performance for different reasoning tasks
when using the updated prompt as the initial prompt.

在这篇论文中，我们提出了一种名为 RePrompt 的新方法，通过从与 LLM 代理的交互中获取的聊天记录，逐步优化 LLM 代理的提示，从而使 LLM 在特定领域中学会规划，我们在 PDDL 生成和旅行规划的实验中证明了我们的方法可以提高不同推理任务的性能。

大规模语言模型代理的自动提示工程规划

RePrompt: Planning by Automatic Prompt Engineering for Large Language  Models Agents

Generative AI models have shown impressive ability to produce images with
text prompts, which could benefit creativity in visual art creation and
self-expression. However, it is unclear how precisely the generated images
express contexts and emotions from the input texts. We explored the emotional
expressiveness of AI-generated images and developed RePrompt, an automatic
method to refine text prompts toward precise expression of the generated
images. Inspired by crowdsourced editing strategies, we curated intuitive text
features, such as the number and concreteness of nouns, and trained a proxy
model to analyze the feature effects on the AI-generated image. With model
explanations of the proxy model, we curated a rubric to adjust text prompts to
optimize image generation for precise emotion expression. We conducted
simulation and user studies, which showed that RePrompt significantly improves
the emotional expressiveness of AI-generated images, especially for negative
emotions.

本研究通过引入特定的测试组合来测试生成的图像能够如何准确地表达其所需的上下文和情感，提出了一种自动方法（RePrompt）来优化文本提示以实现精确情感表达，它基于群体编辑策略和模型解释实现。通过模拟和用户研究发现，RePrompt 明显提高了通过生成的图像表达特定情感的能力。