Summary of Analyzing the Evaluation Of Cross-lingual Knowledge Transfer in Multilingual Language Models, by Sara Rajaee and Christof Monz
Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models
by Sara Rajaee, Christof Monz
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 challenges the assumption that high zero-shot performance on target tasks in multilingual language models reflects their ability to transfer linguistic knowledge across languages. Instead, it shows that observed high performance can be attributed to factors like task- and surface-level knowledge, as well as data artifacts and biases. The authors introduce more challenging evaluation setups involving instances with multiple languages and demonstrate the limitations of existing cross-lingual test data and evaluation methods. This work highlights the importance of a nuanced understanding of multilingual models’ capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multilingual language models can be really good at doing tasks in different languages, but is that because they truly understand those languages or just because they learned how to do certain tasks? Researchers tried to answer this question by looking at what happens when these models are tested on tasks that involve multiple languages. They found that the models’ performance is often due to factors like knowing specific words or sentence structures rather than actually understanding the languages themselves. This means that we need to be more careful when testing how well these models can understand different languages. |
Keywords
* Artificial intelligence * Zero shot
