Summary of Coig-cqia: Quality Is All You Need For Chinese Instruction Fine-tuning, by Yuelin Bai et al.
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
by Yuelin Bai, Xinrun Du, Yiming Liang, Yonggang Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang
First submitted to arxiv on: 26 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 remarkable progress in instruction tuning for large language models (LLMs) has led to improved efficacy and reliability. However, a significant gap remains in instruction tuning for Chinese, which poses complex linguistic challenges. Existing datasets derived from English-centric LLMs are not well-aligned with users’ interaction patterns. To address this gap, the authors introduce COIG-CQIA, a new Chinese instruction tuning dataset derived from various real-world resources and verified by humans. The paper compares models trained on COIG-CQIA to strong baselines and datasets, demonstrating highly competitive performance in diverse benchmarks. Insights are offered for designing effective Chinese instruction-tuning datasets and data-mixing strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have gotten much better at understanding English instructions, but they struggle with Chinese. This is because most training datasets were created using English-based language models, which aren’t very good at understanding Chinese patterns of interaction. To fix this problem, researchers created a new dataset called COIG-CQIA that uses real-world resources and has been verified by humans to make sure it’s accurate. The results show that models trained on this new dataset perform just as well as or even better than other strong models. This is important because it can help improve how we train language models for Chinese, which will allow them to be more helpful in the future. |
Keywords
» Artificial intelligence » Instruction tuning
