[2410.14826] SPRIG: Improving Large Language Model Performance by System Prompt Optimization

Computer Science > Computation and Language arXiv:2410.14826 (cs) [Submitted on 18 Oct 2024 (v1), last revised 4 Apr 2026 (this version, v3)] Title:SPRIG: Improving Large Language Model Performance by System Prompt Optimization Authors:Lechen Zhang, Tolga Ergen, Lajanugen Logeswaran, Moontae Lee, David Jurgens View a PDF of the paper titled SPRIG: Improving Large Language Model Performance by System Prompt Optimization, by Lechen Zhang and 4 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends, in part, on the choice of prompt. Past research has focused on optimizing prompts specific to a task. However, much less attention has been given to optimizing the general instructions included in a prompt, known as a system prompt. To address this gap, we propose SPRIG, an edit-based genetic algorithm that iteratively constructs prompts from prespecified components to maximize the model's performance in general scenarios. We evaluate the performance of system prompts on a collection of 47 different types of tasks to ensure generalizability. Our study finds that a single optimized system prompt performs on par with task prompts optimized for each individual task. Moreover, combining system and task-level optimizations leads to further improvement, which showcases their complementary nature. Experiments also reveal that the optimized system prompts generalize effectively ac...