MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2310.16730v1
• Date: Wed, 25 Oct 2023 15:58:51 GMT
• Title: MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning
• Authors: Dong-Ki Kim, Sungryull Sohn, Lajanugen Logeswaran, Dongsub Shim, Honglak Lee
• Abstract summary: MultiPrompter is a new framework that views prompt optimization as a cooperative game between prompters. We show that MultiPrompter effectively reduces the problem size and helps prompters learn optimal prompts.
• Score: 68.40755873520808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, there has been an increasing interest in automated prompt optimization based on reinforcement learning (RL). This approach offers important advantages, such as generating interpretable prompts and being compatible with black-box foundation models. However, the substantial prompt space size poses challenges for RL-based methods, often leading to suboptimal policy convergence. This paper introduces MultiPrompter, a new framework that views prompt optimization as a cooperative game between prompters which take turns composing a prompt together. Our cooperative prompt optimization effectively reduces the problem size and helps prompters learn optimal prompts. We test our method on the text-to-image task and show its ability to generate higher-quality images than baselines.

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