多语言套利：优化数据池以加速多语言进展
Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress
AI总结：
本文提出“多语言套利”方法，通过策略性路由样本至多样化模型池，利用多模型在不同语言上的性能差异，有效解决了单一教师模型的局限，在多语言任务中实现高达56.5%的胜率提升。
摘要：
合成数据的使用在最近的最先进突破中发挥了关键作用。然而，过度依赖单一的预言机教师模型来生成数据已被证明会导致模型崩溃并引发偏见的传播。这些局限性在多语言环境中尤为明显，因为缺乏在所有语言中均表现优异的普遍有效的教师模型带来了重大挑战。在这项工作中，我们通过引入“多语言套利”来解决这些极端差异，该策略利用多个模型在给定语言上的性能差异。为此，我们策略性地通过多样化的模型池来路由样本，每个模型在不同语言上具有独特的优势。在对最先进模型进行的详尽实验中，我们的工作表明，套利技术能够带来显著的性能提升，远超依赖单一教师的效果。具体而言，与最佳单一教师相比，我们观察到在切换到多语言套利时，所有语言平均胜率提升了高达56.5%。我们在模型池中资源最匮乏的语言上观察到了最显著的提升。
英文摘要：
The use of synthetic data has played a critical role in recent state-of-art breakthroughs. However, overly relying on a single oracle teacher model to generate data has been shown to lead to model collapse and invite propagation of biases. These limitations are particularly evident in multilingual settings, where the absence of a universally effective teacher model that excels across all languages presents significant challenges. In this work, we address these extreme difference by introducing "multilingual arbitrage", which capitalizes on performance variations between multiple models for a given language. To do so, we strategically route samples through a diverse pool of models, each with unique strengths in different languages. Across exhaustive experiments on state-of-art models, our work suggests that arbitrage techniques allow for spectacular gains in performance that far outperform relying on a single teacher. In particular, compared to the best single teacher, we observe gains of up to 56.5% improvement in win rates averaged across all languages when switching to multilingual arbitrage. We observe the most significant gains for the least resourced languages in our pool.
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。
