Prompt Optimization with Logged Bandit Data
Haruka Kiyohara · Yuta Saito · Daniel Cao · Thorsten Joachims
Abstract
We study how to use naturally available user feedback, such as clicks, to optimize a prompt policy for generating sentences with large language models (LLMs). Naive approaches, including regression-based and importance sampling-based ones, suffer either from biased log data or variance caused by the large action space of prompt. To circumvent these challenges, we propose a way to leverage similarity and smoothness in the (generated) sentence embedding space, substantially reducing variance in the policy gradients while maintaining a small bias. Initial experiments on synthetic data demonstrate the effectiveness of our approach. We also plan to publish the extended benchmark and simulator as open-source software.
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