Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment

TL;DR

This paper presents a theoretical and empirical framework for prompt optimization to align large language models with human values, offering an efficient alternative to traditional fine-tuning methods like RLHF.

Contribution

It formulates prompt optimization as an optimization problem, providing theoretical bounds and insights, and validates its effectiveness through experiments.

Findings

Prompt optimization can effectively align LLMs without parameter fine-tuning.

Theoretical bounds on suboptimality depend on the prompt and model.

Empirical results show successful alignment across various datasets.

Abstract

The alignment of large language models (LLMs) with human values is critical as these models become increasingly integrated into various societal and decision-making processes. Traditional methods, such as reinforcement learning from human feedback (RLHF), achieve alignment by fine-tuning model parameters, but these approaches are often computationally expensive and impractical when models are frozen or inaccessible for parameter modification. In contrast, prompt optimization is a viable alternative to RLHF for LLM alignment. While the existing literature has shown empirical promise of prompt optimization, its theoretical underpinning remains under-explored. We address this gap by formulating prompt optimization as an optimization problem and try to provide theoretical insights into the optimality of such a framework. To analyze the performance of the prompt optimization, we study theoretical suboptimality bounds and provide insights in terms of how prompt optimization depends upon the given prompter and target model. We also provide empirical validation through experiments on various datasets, demonstrating that prompt optimization can effectively align LLMs, even when parameter fine-tuning is not feasible.