Summary of Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models, by Sina Bagheri Nezhad et al.
Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models
by Sina Bagheri Nezhad, Ameeta Agrawal, Rhitabrat Pokharel
First submitted to arxiv on: 17 Dec 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 Multilingual language models (MLLMs) play a crucial role in handling text across various languages, but they often exhibit performance disparities due to differences in resource availability and linguistic characteristics. Our study reveals additional critical factors that significantly influence MLLM effectiveness, including token similarity and country similarity, which facilitate cross-lingual transfer and highlight the importance of shared cultural and linguistic contexts. We analyzed a wide range of features, such as geographical, linguistic, and resource-related aspects, on the SIB-200 dataset for classification and the Flores-200 dataset for machine translation, using regression models and SHAP values across 204 languages. Our findings offer valuable guidance for developing more equitable and effective multilingual language models, particularly for underrepresented languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computers can understand text in different languages. Right now, these machines are good at understanding some languages better than others. The researchers wanted to find out why this is happening. They discovered that two important factors are the similarity of words and phrases between languages and the cultural context of each language. This helps them understand how to make computers better at understanding text in all languages, not just a few popular ones. |
Keywords
» Artificial intelligence » Classification » Regression » Token » Translation
