用多语言提示激发模型文化多样性，提升生成内容丰富度。
Multilingual Prompting for Improving LLM Generation Diversity
- 通过多语言提示注入不同文化语境，激活模型中的多元知识。
- 在多个大模型上均优于高温采样等传统方法，显著提升多样性。
- 适配文化与语言一致可减少虚构文化信息，适合跨文化应用。
大型语言模型在生成内容时普遍存在文化代表性不足和多样性欠缺的问题，无论是在表达观点还是回答事实问题时。为此，我们提出多语言提示：一种通过在基础提示中加入多种文化的语言与文化线索，生成多个提示变体，获取响应并整合结果的提示方法。基于大模型具有语言特异性知识的证据，该方法旨在通过激活训练数据中更广泛的文化知识来增强多样性。在 GPT-4o、GPT-4o-mini、LLaMA 70B 和 LLaMA 8B 多个模型上的实验表明，多语言提示在多数情况下优于高温采样、逐步回忆和角色提示等现有多样性增强技术。进一步分析显示，其效果在高/低资源语言间存在差异，且提示语言与文化线索对齐可有效降低关于特定文化信息的幻觉。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are known to lack cultural representation and overall diversity in their generations, from expressing opinions to answering factual questions. To mitigate this problem, we propose multilingual prompting: a prompting method which generates several variations of a base prompt with added cultural and linguistic cues from several cultures, generates responses, and then combines the results. Building on evidence that LLMs have language-specific knowledge, multilingual prompting seeks to increase diversity by activating a broader range of cultural knowledge embedded in model training data. Through experiments across multiple models (GPT-4o, GPT-4o-mini, LLaMA 70B, and LLaMA 8B), we show that multilingual prompting consistently outperforms existing diversity-enhancing techniques such as high-temperature sampling, step-by-step recall, and persona prompting. Further analyses show that the benefits of multilingual prompting vary between high and low resource languages and across model sizes, and that aligning the prompting language with cultural cues reduces hallucination about culturally-specific information.
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