Summary of Rlhf Can Speak Many Languages: Unlocking Multilingual Preference Optimization For Llms, by John Dang et al.
RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs
by John Dang, Arash Ahmadian, Kelly Marchisio, Julia Kreutzer, Ahmet Üstün, Sara Hooker
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper presents an exhaustive study on aligning multilingual large language models (LLMs). The authors introduce a novel method for generating high-quality feedback data to balance coverage across languages. They demonstrate the benefits of cross-lingual transfer and increased dataset size in preference training, achieving state-of-the-art results in 23 languages covering half of the world’s population. The paper shows that their preference-trained model outperforms current state-of-the-art models like Aya 23 8B, Gemma-1.1-7B-it, Llama-3-8B-Instruct, and Mistral-7B-Instruct-v0.3 in multilingual settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making language models work with many languages at the same time. The authors created a new way to make these models better by giving them more information from different languages. They tested their method and found that it works well, even beating some of the best existing models. This study helps us understand how to use language models in real-life situations where people speak different languages. |
Keywords
» Artificial intelligence » Llama
