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