Summary of Rethinking Llm Language Adaptation: a Case Study on Chinese Mixtral, by Yiming Cui et al.
Rethinking LLM Language Adaptation: A Case Study on Chinese Mixtral
by Yiming Cui, Xin Yao
First submitted to arxiv on: 4 Mar 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 paper proposes Chinese-Mixtral and Chinese-Mixtral-Instruct, which are language models designed to improve the understanding and generation of Chinese text while retaining their original English abilities. The authors base their approach on Mixtral-8x7B-v0.1, a representative sparse mixture of experts (SMoE) language model that has received significant attention due to its unique design and superior performance. Experimental results show that the proposed models successfully improve Chinese understanding and generation while maintaining their English abilities. The authors also discuss key questions when adapting large language models to new languages, including extending vocabulary and choosing initialization models. They provide empirical results and analysis to support their claims. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making a special kind of computer program, called Mixtral, work better for Chinese words. Currently, Mixtral can understand and create English text well, but it’s not very good at understanding or creating Chinese text. The authors created new versions of Mixtral, called Chinese-Mixtral and Chinese-Mixtral-Instruct, that are specifically designed to improve their ability to work with Chinese words. They tested these models and found that they can understand and create Chinese text better than the original Mixtral. The authors also talked about some important questions when making language models like this one work for new languages. |
Keywords
» Artificial intelligence » Attention » Language model » Mixture of experts
