Summary of Xcot: Cross-lingual Instruction Tuning For Cross-lingual Chain-of-thought Reasoning, by Linzheng Chai et al.
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning
by Linzheng Chai, Jian Yang, Tao Sun, Hongcheng Guo, Jiaheng Liu, Bing Wang, Xiannian Liang, Jiaqi Bai, Tongliang Li, Qiyao Peng, Zhoujun Li
First submitted to arxiv on: 13 Jan 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 This paper proposes a framework for transferring knowledge from high-resource languages to low-resource languages using chain-of-thought (CoT) techniques. The CoT technique excels at eliciting reasoning in large language models and improves various downstream tasks, but its performance is constrained when applied to low-resource languages due to poor language generalization. To bridge this gap, the authors introduce a cross-lingual instruction fine-tuning framework (xCOT), which includes multilingual instruction training data (xCOT-INSTRUCT) for semantic alignment across languages. The xCOT also employs cross-lingual in-context few-shot learning (xICL) to accelerate agreement and randomly online CoT strategy to enhance multilingual reasoning. Furthermore, the authors leverage high-resource CoT to supervise low-resource language training with cross-lingual distillation. Experimental results demonstrate superior performance of xCoT in reducing linguistic gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make computers better at understanding different languages. Right now, it’s hard for them to learn new languages because they’re mostly trained on English and a few other popular languages. The authors propose a way to transfer knowledge from these high-resource languages to low-resource languages, like Arabic or Hindi. They use a technique called chain-of-thought (CoT) that makes computers better at reasoning and understanding language. To make it work across different languages, they create a special training set and then use a type of machine learning called cross-lingual distillation to help the computer learn from the other languages. The results show that their approach is more effective than current methods in closing the gap between high-resource and low-resource languages. |
Keywords
» Artificial intelligence » Alignment » Distillation » Few shot » Fine tuning » Generalization » Machine learning
