Summary of Sambalingo: Teaching Large Language Models New Languages, by Zoltan Csaki et al.
SambaLingo: Teaching Large Language Models New Languages
by Zoltan Csaki, Bo Li, Jonathan Li, Qiantong Xu, Pian Pawakapan, Leon Zhang, Yun Du, Hengyu Zhao, Changran Hu, Urmish Thakker
First submitted to arxiv on: 8 Apr 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 The paper presents a comprehensive investigation into the adaptation of large language models (LLMs) to new languages. The authors aim to address the gap in LLM capabilities and availability across diverse languages by adapting existing pre-trained LLMs on new languages. They explore key components, including vocabulary extension, direct preference optimization, and data scarcity for human alignment in low-resource languages. The study scales experiments across 9 languages and 2 parameter scales (7B and 70B), comparing models against popular LLMs like Llama 2, Aya-101, XGLM, BLOOM, and existing language experts. Notably, the authors outperform prior published baselines and make their evaluation code and checkpoints publicly available to facilitate future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make large language models work better for people who don’t speak English or other popular languages. They take a pre-trained model and teach it new words and rules in another language. The authors tested this process on 9 different languages and found that their method works really well, even when there’s not much data available. They compared their results to other models and experts, and theirs were the best so far. Now, they’re sharing their code and model with others so more research can be done. |
Keywords
* Artificial intelligence * Alignment * Llama * Optimization
