Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress

TL;DR

This paper introduces multilingual arbitrage, a method that leverages performance differences among multiple models to improve multilingual data generation, resulting in significant performance gains especially for low-resource languages.

Contribution

The work proposes a novel multilingual arbitrage technique that uses multiple models to enhance data quality and model performance across diverse languages.

Findings

Up to 56.5% improvement in win rates across languages.

Significant gains for low-resource languages.

Outperforms single-teacher approaches.

Abstract

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.