用翻译链提升低资源语言的模型表现，尤其在识别仇恨言论上效果显著。
Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages
- 输入先译成英语再处理，最后可回译，全在单一提示中完成。
- 在马拉地语上测试，仇恨言论检测准确率提升最明显。
- 适合需要提升低资源语言性能的研究者和开发者。
本文提出链式翻译提示（CoTR），一种针对低资源语言增强语言模型性能的新策略。CoTR将输入从低资源语言（如马拉地语）先翻译成高资源语言（如英语），在英文文本上执行任务（如生成、分类），必要时再将输出回译回原语言，所有步骤均在单个提示中指定。我们在马拉地语上对情感分析、仇恨言论分类、主题分类和文本生成等任务进行了验证，结果表明该方法显著优于传统提示方式，尤其在仇恨言论检测任务中提升最为突出。该技术还具备提升低资源语言合成数据质量的潜力，为未来多语言大模型应用提供新思路。
原文摘要 · Abstract (English)
This paper introduces Chain of Translation Prompting (CoTR), a novel strategy designed to enhance the performance of language models in low-resource languages. CoTR restructures prompts to first translate the input context from a low-resource language into a higher-resource language, such as English. The specified task like generation, classification, or any other NLP function is then performed on the translated text, with the option to translate the output back to the original language if needed. All these steps are specified in a single prompt. We demonstrate the effectiveness of this method through a case study on the low-resource Indic language Marathi. The CoTR strategy is applied to various tasks, including sentiment analysis, hate speech classification, subject classification and text generation, and its efficacy is showcased by comparing it with regular prompting methods. Our results underscore the potential of translation-based prompting strategies to significantly improve multilingual LLM performance in low-resource languages, offering valuable insights for future research and applications. We specifically see the highest accuracy improvements with the hate speech detection task. The technique also has the potential to enhance the quality of synthetic data generation for underrepresented languages using LLMs.
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