SPRIG: Improving Large Language Model Performance by System Prompt Optimization (2410.14826v2)

Abstract: 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 across model families, parameter sizes, and languages. This study provides insights into the role of system-level instructions in maximizing LLM potential.

Essay on "Sprig: Improving LLM Performance by System Prompt Optimization"

The paper "Sprig: Improving LLM Performance by System Prompt Optimization" introduces a novel approach to enhancing the efficacy of LLMs through the optimization of system-level prompts. While traditional prompt optimization has predominantly targeted task-specific instructions, this research shifts focus to generic system prompts, aiming to establish a task-agnostic prompting strategy that can enhance model performance across various scenarios.

Key Contributions

The authors propose an edit-based genetic algorithm, named System Prompt Refinement for Increased Generalization (Sprig), which systematically builds and refines system prompts using prespecified components. This refined system prompt demonstrates performance on par with individually optimized task-specific prompts, showcasing its potential as a robust, generalizable approach.

1. Optimization Framework:
   • Sprig utilizes a genetic algorithm-inspired approach to refine system prompts, leveraging a comprehensive corpus of prompt components. This includes categories such as Chain-of-Thought (CoT) reasoning, role-based instructions, and emotional cues, facilitating broad applicability across multiple tasks.
   • The framework includes operations like addition, rephrasing, swapping, and deletion of prompt components to explore a vast search space efficiently. An Upper Confidence Bound (UCB) method is used to manage this search space, focusing on components with higher potential for improvement.
2. Experimental Evaluation:
   • The paper spans 47 diverse tasks, covering domains such as reasoning, mathematics, social understanding, and commonsense.
   • Findings reveal that optimized system prompts significantly outperform traditional CoT prompts and can be used in conjunction with task-specific optimizations for even greater efficacy.
3. Generalization Capabilities:
   • Notably, the optimized system prompts generalize well across different model families, parameter sizes, and languages, outperforming task-optimized prompts in non-target languages and demonstrating limited effects when scaling to larger models.
4. Complementary Nature of System and Task Prompts:
   • The experiments indicated that system and task optimizations target complementary strategies, allowing their combination to yield superior overall performance.

Implications and Future Directions

The results from this paper suggest significant implications for both practical deployment and theoretical understanding of LLMs:

• Practical Implications:
  • System prompt optimization as introduced by Sprig provides an efficient, scalable solution for enhancing LLMs in a resource-constrained environment. Its ability to generalize across tasks and languages without the need for extensive retraining makes it an attractive approach for real-world applications.
• Theoretical Significance:
  • The research underscores the importance of exploring the role of generic system instructions, rather than focusing solely on task-specific optimizations. This shift in strategy could pave the way for developing more intuitive and adaptable AI systems.
• Future Research:
  • Further exploration of system prompt optimization for larger LLMs could unlock new insights into scaling behaviors and optimization efficiencies.
  • Expanding the diversity and adaptability of prompt components could enhance the robustness and applicability of system-level instructions across additional tasks and domains.
  • Integration with adaptive methods for corpus expansion could automate and refine the optimization process, minimizing manual intervention and bias.

In summary, the paper presents a compelling argument for the potential of system prompt optimization as a versatile tool in enhancing LLM performance. The innovative approach of Sprig, combined with its proven generalization capabilities, marks a significant step forward in AI research, opening up new pathways for the development of more versatile and efficient LLMs.