大语言模型是优秀的多语言学习者：当大语言模型遇上跨语言提示
Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts
AI总结：
针对LLMs在长复杂上下文规则遵循中易遗漏规则的问题，提出多语言提示策略MLPrompt，通过翻译易错规则增强关注，实验优于现有提示方法，并扩展至结构化数据生成任务。
摘要：
随着大语言模型（LLMs）的出现，为现实应用生成基于规则的数据变得更加容易。由于自然语言固有的模糊性和规则集的复杂性，尤其是在长上下文场景中，LLMs往往难以遵循所有指定规则，经常至少遗漏一条。为了增强LLMs在长且复杂上下文下的推理和理解能力，我们提出一种新颖的提示策略——多语言提示（MLPrompt），它会自动将LLMs难以遵循的易错规则翻译成另一种语言，从而使其获得更多关注。在不同任务的公共数据集上的实验结果表明，MLPrompt能优于思维链、思维树和自一致性等最先进的提示方法。此外，我们引入一个集成MLPrompt与自动检查机制的框架用于结构化数据生成，并以文本到MIP实例为具体案例研究。进一步，我们扩展该框架至文本到SQL任务，以展示其在结构化数据合成方面的生成能力。
英文摘要：
With the advent of Large Language Models (LLMs), generating rule-based data for real-world applications has become more accessible. Due to the inherent ambiguity of natural language and the complexity of rule sets, especially in long contexts, LLMs often struggle to follow all specified rules, frequently omitting at least one. To enhance the reasoning and understanding of LLMs on long and complex contexts, we propose a novel prompting strategy Multi-Lingual Prompt, namely MLPrompt, which automatically translates the error-prone rule that an LLM struggles to follow into another language, thus drawing greater attention to it. Experimental results on public datasets across various tasks have shown MLPrompt can outperform state-of-the-art prompting methods such as Chain of Thought, Tree of Thought, and Self-Consistency. Additionally, we introduce a framework integrating MLPrompt with an auto-checking mechanism for structured data generation, with a specific case study in text-to-MIP instances. Further, we extend the proposed framework for text-to-SQL to demonstrate its generation ability towards structured data synthesis.
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。
