Summary of Sailor: Open Language Models For South-east Asia, by Longxu Dou et al.
Sailor: Open Language Models for South-East Asia
by Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo, Jiahui Zhou, Wei Lu, Min Lin
First submitted to arxiv on: 4 Apr 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 Sailor is a family of open language models designed specifically for South-East Asian languages, with parameters ranging from 0.5B to 7B. These models are pre-trained from Qwen1.5 and can accept up to 400B tokens. The training process involves techniques like BPE dropout, data cleaning, deduplication, and small proxy models. Experimental results show that Sailor models perform well across four typical tasks: commonsense reasoning, question answering, reading comprehension, and examination. This report aims to share insights and spark interest in developing large language models for multilingual use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sailor is a special group of language models made just for languages spoken in South-East Asia. These models are very good at understanding and generating text in these languages. They’re trained using a combination of techniques that help them learn to be more robust and accurate. The results show that Sailor models do well on tasks like answering questions, understanding text, and taking exams. This report is about sharing the knowledge gained from developing these models and encouraging others to work on similar projects. |
Keywords
» Artificial intelligence » Dropout » Question answering
