Summary of The Impact Of Demonstrations on Multilingual In-context Learning: a Multidimensional Analysis, by Miaoran Zhang et al.
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis
by Miaoran Zhang, Vagrant Gautam, Mingyang Wang, Jesujoba O. Alabi, Xiaoyu Shen, Dietrich Klakow, Marius Mosbach
First submitted to arxiv on: 20 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 paper explores multilingual in-context learning, where large language models are trained to perform tasks using only a few labeled demonstrations without needing any parameter updates. The authors conduct a multidimensional analysis using 5 different model families, 9 datasets covering classification and generation tasks, and 56 typologically diverse languages. They find that the effectiveness of demonstrations varies significantly across models, tasks, and languages, and that some strong instruction-following models are largely insensitive to the quality of demonstrations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well language models can learn new tasks just from a few examples, without needing any special training. It tries different models, datasets, and languages to see if it can find patterns about when this kind of learning works best. The results show that what helps or hurts depends on many factors, including the type of model being used and the language it’s trying to understand. |
Keywords
» Artificial intelligence » Classification
