title: MARS: A Multi-Agent Framework Incorporating Socratic Guidance for
    Automated Prompt Optimization
summary: 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.
paper_authors._id: 67e0c1ce151ca9ed9284dc52
paper_authors.name: Jian Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-24T08:07:05.271Z
paper_authors._id: 658be7fe135580745c510323
paper_authors.avatarUrl: /avatars/830e5cec4565efdc23226a86a0fcef0e.svg
paper_authors.fullname: Jian Zhang
paper_authors.type: user
paper_authors.user: VentureZJ
paper_authors._id: 67e0c1ce151ca9ed9284dc53
paper_authors.name: Zhangqi Wang
paper_authors._id: 67e0c1ce151ca9ed9284dc54
paper_authors.name: Haiping Zhu
paper_authors._id: 67e0c1ce151ca9ed9284dc55
paper_authors.name: Jun Liu
paper_authors._id: 67e0c1ce151ca9ed9284dc56
paper_authors.name: Qika Lin
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-24T02:22:15.812Z
paper_authors._id: 66ac77011cfb12c087605acb
paper_authors.avatarUrl: /avatars/54c06bd1c4c9d491470ed4162c2301ae.svg
paper_authors.fullname: Lin
paper_authors.type: user
paper_authors.user: Qika
paper_authors._id: 67e0c1ce151ca9ed9284dc57
paper_authors.name: Erik Cambria
paper_title: MARS: A Multi-Agent Framework Incorporating Socratic Guidance for
    Automated Prompt Optimization
paper_summary: 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.
paper_ai_summary: A proposed Multi-Agent framework with Socratic guidance optimizes prompts for large language models by incorporating flexibility and efficient search mechanisms.