PRESTO: Preimage-Informed Instruction Optimization for Prompting Black-Box LLMs

TL;DR

PRESTO introduces a novel method leveraging preimage structures to optimize instructions for black-box LLMs more efficiently, significantly reducing query costs and improving performance across multiple tasks.

Contribution

It reinterprets the many-to-one soft prompt mapping as a useful prior, proposing PRESTO with three components to enhance instruction optimization efficiency.

Findings

Achieves 14x more scored data with the same query budget

Outperforms existing methods on 33 instruction tasks

Demonstrates superior optimization efficiency and effectiveness

Abstract

Large language models (LLMs) have achieved remarkable success across diverse domains, due to their strong instruction-following capabilities. This has led to increasing interest in optimizing instructions for black-box LLMs, whose internal parameters are inaccessible but widely used due to their strong performance. To optimize instructions for black-box LLMs, recent methods employ white-box LLMs to generate candidate instructions from optimized soft prompts. However, white-box LLMs often map different soft prompts to the same instruction, leading to redundant queries. While previous studies regarded this many-to-one mapping as a structure that hinders optimization efficiency, we reinterpret it as a useful prior knowledge that can accelerate the optimization. To this end, we introduce PREimage-informed inSTruction Optimization (PRESTO), a novel framework that leverages the preimage structure of soft prompts for efficient optimization. PRESTO consists of three key components: (1) score sharing, which shares the evaluation score with all soft prompts in a preimage; (2) preimage-based initialization, which selects initial data points that maximize search space coverage using preimage information; and (3) score consistency regularization, which enforces prediction consistency within each preimage. By leveraging preimages, PRESTO achieves the effect of effectively obtaining 14 times more scored data under the same query budget, resulting in more efficient optimization. Experimental results on 33 instruction optimization tasks demonstrate the superior performance of PRESTO. Code is available at https://github.com/mlvlab/PRESTO