Summary of Crossin: An Efficient Instruction Tuning Approach For Cross-lingual Knowledge Alignment, by Geyu Lin et al.
CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment
by Geyu Lin, Bin Wang, Zhengyuan Liu, Nancy F. Chen
First submitted to arxiv on: 18 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 This paper addresses the challenge of large language models (LLMs) being English-centric and performing poorly in other languages. The issue lies in the imbalanced distribution of training data across languages during pre-training and instruction tuning stages. To overcome this, the authors propose CrossIn, a novel approach that combines cross-lingual instruction tuning data to enhance the model’s task-solving capabilities and multilingual proficiency within a single process. This method leverages shared compressed representations between languages to improve performance across tasks and languages. The paper also introduces a multi-task and multi-faceted benchmark to evaluate the effectiveness of CrossIn, which demonstrates substantial improvements in experimental results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better at understanding many languages, not just English. Right now, language models are good at English but struggle with other languages that are very different from English. The problem is that they’re trained on more data for English than for other languages. To fix this, the authors suggest a new way of training called CrossIn, which uses data from many languages to make the model better at all languages. They also created a special test to see how well CrossIn works, and it shows big improvements. |
Keywords
» Artificial intelligence » Instruction tuning » Multi task
