Summary of Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress, by Ayomide Odumakinde et al.
Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress
by Ayomide Odumakinde, Daniel D’souza, Pat Verga, Beyza Ermis, Sara Hooker
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach called “multilingual arbitrage” is introduced, which leverages performance variations between multiple language models to achieve spectacular gains in performance. By strategically routing samples through a diverse pool of models, each with unique strengths in different languages, the method capitalizes on performance differences to overcome model collapse and bias propagation issues. Experimental results show that multilingual arbitrage outperforms relying on a single teacher model by up to 56.5% improvement in win rates averaged across all languages, particularly benefiting less-resourced languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has made great progress thanks to synthetic data. However, using just one “teacher” model can cause problems like poor performance and spreading biases. This is especially true when dealing with many different languages. A new idea called “multilingual arbitrage” helps solve these issues by mixing in data from multiple models that are good at different languages. By doing this, the approach gets much better results than using just one teacher model. In fact, it’s up to 56.5% better! |
Keywords
» Artificial intelligence » Machine learning » Synthetic data » Teacher model
