用多语言提示让大模型更准地理解复杂规则
Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts
- 将难懂的规则自动翻译成其他语言，吸引模型注意
- 在多个任务上超越思维链等先进提示方法
- 适合需要精准生成结构化数据的场景
随着大语言模型（LLMs）的发展，生成适用于现实应用的规则数据变得更加容易。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下，LLMs 常常无法遵循所有指定规则，经常遗漏至少一条。为提升 LLMs 在长且复杂上下文中的推理与理解能力，我们提出一种新型提示策略——多语言提示（MLPrompt），该策略将模型难以遵循的易错规则自动翻译成另一种语言，从而增强其关注。在多个公开数据集上的实验表明，MLPrompt 在各类任务中均优于当前领先的提示方法，如思维链（Chain of Thought）、思维树（Tree of Thought）和自一致性（Self-Consistency）。此外，我们构建了一个集成 MLPrompt 与自动校验机制的框架，用于结构化数据生成，并以文本到 MIP 实例为例进行案例研究。进一步，我们将该框架拓展至文本到 SQL 任务，验证其在结构化数据合成方面的生成能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。