MARS: A Multi-Agent Framework Incorporating Socratic Guidance for
  Automated Prompt Optimization
Authors: name: Jian Zhang, name: Zhangqi Wang, name: Haiping Zhu, name: Jun Liu, name: Qika Lin, name: Erik Cambria

The basic question-answering format of large language models involves
inputting a prompt and receiving a response, and the quality of the prompt
directly impacts the effectiveness of the response. Automated Prompt
Optimization (APO) aims to break free from the cognitive biases of manually
designed prompts and explores a broader design space for prompts. However,
existing APO methods suffer from limited flexibility of fixed templates and
inefficient search in prompt spaces as key issues. To this end, we propose a
Multi-Agent framework Incorporating Socratic guidance (MARS), which utilizes
multi-agent fusion technology for automatic planning, with gradual continuous
optimization and evaluation. Specifically, MARS comprises seven agents, each
with distinct functionalities, which autonomously use the Planner to devise an
optimization path that ensures flexibility. Additionally, it employs a
Teacher-Critic-Student Socratic dialogue pattern to iteratively optimize the
prompts while conducting effective search. We conduct extensive experiments on
various datasets to validate the effectiveness of our method, and perform
additional analytical experiments to assess the model's advancement as well as
the interpretability.