Summary of Language Imbalance Driven Rewarding For Multilingual Self-improving, by Wen Yang et al.
Language Imbalance Driven Rewarding for Multilingual Self-improving
by Wen Yang, Junhong Wu, Chen Wang, Chengqing Zong, Jiajun Zhang
First submitted to arxiv on: 11 Oct 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 The proposed approach, Language Imbalance Driven Rewarding (LIDR), leverages the inherent imbalance between dominant and non-dominant languages within Large Language Models (LLMs) as a reward signal to bootstrap their multilingual capabilities. This iterative process demonstrates improved performance in both dominant and non-dominant languages, yielding an average improvement of 7.46% win rate on the X-AlpacaEval leaderboard and 13.9% accuracy on the MGSM benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have made great progress, but they mostly help “first-class” languages like English and Chinese. Many other languages are left behind. This creates an imbalance that can be used to make LLMs better at many languages at once. The researchers propose a new way to do this, called Language Imbalance Driven Rewarding (LIDR). They train their model using this approach and show that it gets better and better. It’s like a self-improvement loop! |
