Large language models (LLMs) have showcased remarkable potential across
various tasks by conditioning on prompts. However, the quality of different
human-written prompts leads to substantial discrepancies in LLMs' performance,
and improving prompts usually necessitates considerable human effort and
expertise. To this end, this paper proposes Prompt with Actor-Critic Editing
(PACE) for LLMs to enable automatic prompt editing. Drawing inspiration from
the actor-critic algorithm in reinforcement learning, PACE leverages LLMs as
the dual roles of actors and critics, conceptualizing prompt as a type of
policy. PACE refines prompt, taking into account the feedback from both actors
performing prompt and critics criticizing response. This process helps LLMs
better align prompt to a specific task, thanks to real responses and thinking
from LLMs. We conduct extensive experiments on 24 instruction induction tasks
and 21 big-bench tasks. Experimental results indicate that PACE elevates the
relative performance of medium/low-quality human-written prompts by up to 98\%,
which has comparable performance to high-quality human-written prompts.
Moreover, PACE also exhibits notable efficacy for prompt generation.

通过自动提示编辑技术（PACE），将大型语言模型（LLMs）自动优化为特定任务的更好提示，提高了中低质量的人工编写提示的性能，并具有与高质量提示相当的性能.

PACE：基于演员 - 评论家编辑技术改进大型语言模型的提示

PACE: Improving Prompt with Actor-Critic Editing for Large Language  Model

The field of text-to-image (T2I) generation has garnered significant
attention both within the research community and among everyday users. Despite
the advancements of T2I models, a common issue encountered by users is the need
for repetitive editing of input prompts in order to receive a satisfactory
image, which is time-consuming and labor-intensive. Given the demonstrated text
generation power of large-scale language models, such as GPT-k, we investigate
the potential of utilizing such models to improve the prompt editing process
for T2I generation. We conduct a series of experiments to compare the common
edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting
T2I, and examine factors that may influence this process. We found that GPT-k
models focus more on inserting modifiers while humans tend to replace words and
phrases, which includes changes to the subject matter. Experimental results
show that GPT-k are more effective in adjusting modifiers rather than
predicting spontaneous changes in the primary subject matters. Adopting the
edit suggested by GPT-k models may reduce the percentage of remaining edits by
20-30%.

该研究使用 GPT-k 等大型语言模型来改进文本到图像生成中的提示编辑过程，发现 GPT-k 模型更专注于插入修改词语，从而减少 20-30% 的剩余编辑。