Summary of How Do Large Language Models Handle Multilingualism?, by Yiran Zhao et al.
How do Large Language Models Handle Multilingualism?
by Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi, Lidong Bing
First submitted to arxiv on: 29 Feb 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 study explores the multilingual capabilities of large language models (LLMs) by analyzing their internal workings and developing a novel framework called . It is hypothesized that LLMs initially understand queries in English, then switch to multilingual processing in intermediate layers, and finally generate responses aligned with the original query language. The researchers introduce to identify activated neurons for inputs in different languages without labeled data, validating through experiments on various tasks. This approach improves multilingual abilities by fine-tuning language-specific neurons with a small dataset, achieving average improvements of 3.6% and 2.3% for high- and low-resource languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart at understanding many different languages. Scientists looked inside these models to see how they work with languages that aren’t English. They found out that the models start by understanding the question in English, then switch to using multiple languages when solving problems. The researchers created a new way to figure out which parts of the model are used for different languages without needing special labels. This helps the models get even better at working with many languages. |
Keywords
» Artificial intelligence » Fine tuning
