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Oral
in
Workshop: Third Workshop on Efficient Natural Language and Speech Processing (ENLSP-III): Towards the Future of Large Language Models and their Emerging Descendants

[Paper-Oral 7] MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning

Dong-Ki Kim · Sungryull Sohn · Lajanugen Logeswaran · Dongsub Shim · Honglak Lee


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 who 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 demonstrate its ability to generate higher-quality images than baselines.

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