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

2025-01-07

AI Summary

Value alignment for frozen large language models (LLMs) remains challenging due to the absence of parameter updates. Method: This paper proposes the first optimization-theoretic framework for automatic prompt optimization—requiring no parameter fine-tuning. We establish a rigorous theoretical analysis, deriving—for the first time—the suboptimality bound for prompt optimization and revealing the intrinsic dependence of alignment capability on the interplay between the prompter and the target model. Contribution/Results: Extensive experiments across multiple benchmarks demonstrate that our method achieves alignment performance comparable to RLHF while drastically reducing computational overhead; it is fully compatible with black-box and frozen-model settings. The core contribution lies in bridging a critical theoretical gap in prompt-based alignment, yielding a novel paradigm that is interpretable, computationally efficient, and practically deployable.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Large Language Models
• Instruction Following
• Prompt Optimization

Innovation

• Align-Pro
• Prompt Optimization
• Large Language Models