跨语言提示引导性：迈向跨语言的准确且稳健的大语言模型行为
Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages
AI总结：
本文提出了一种统一的四维评估框架，通过大规模实验揭示了提示组件对跨语言行为的影响，并开发了提示优化框架以提升多语言LLM的准确性和稳健性。
摘要：
系统提示为在推理时间条件下大语言模型（LLMs）提供了一种轻量级却强大的机制。尽管先前的工作集中在英语-only设置上，但现实世界部署需要一个能够可靠地在多种语言中运行的单一提示。本文全面研究了不同系统提示如何引导模型朝着准确且稳健的跨语言行为发展。我们提出了一种统一的四维评估框架，以评估系统提示在多语言环境中的表现。通过在五种语言、三种LLMs和三种基准上的大规模实验，我们发现某些提示组件，如CoT、情绪和场景，与稳健的多语言行为相关。我们开发了一种适用于多语言设置的提示优化框架，并展示了它能够自动发现提高所有指标5-10%的提示。最后，我们分析了超过1000万个推理单元，并发现更高效的系统提示会引发更结构化和一致的推理模式，同时减少不必要的语言切换。总之，我们强调系统提示优化作为实现准确且稳健多语言LLM行为的可扩展路径。
英文摘要：
System prompts provide a lightweight yet powerful mechanism for conditioning large language models (LLMs) at inference time. While prior work has focused on English-only settings, real-world deployments benefit from having a single prompt to operate reliably across languages. This paper presents a comprehensive study of how different system prompts steer models toward accurate and robust cross-lingual behavior. We propose a unified four-dimensional evaluation framework to assess system prompts in multilingual environments. Through large-scale experiments on five languages, three LLMs, and three benchmarks, we uncover that certain prompt components, such as CoT, emotion, and scenario, correlate with robust multilingual behavior. We develop a prompt optimization framework for multilingual settings and show it can automatically discover prompts that improve all metrics by 5-10%. Finally, we analyze over 10 million reasoning units and find that more performant system prompts induce more structured and consistent reasoning patterns, while reducing unnecessary language-switching. Together, we highlight system prompt optimization as a scalable path to accurate and robust multilingual LLM behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
