Summary of Towards Multilingual Llm Evaluation For European Languages, by Klaudia Thellmann et al.
Towards Multilingual LLM Evaluation for European Languages
by Klaudia Thellmann, Bernhard Stadler, Michael Fromm, Jasper Schulze Buschhoff, Alex Jude, Fabio Barth, Johannes Leveling, Nicolas Flores-Herr, Joachim Köhler, René Jäkel, Mehdi Ali
First submitted to arxiv on: 11 Oct 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 proposes a novel approach for evaluating Large Language Models (LLMs) across multiple European languages. By employing translated versions of five widely-used benchmarks, the authors assess the capabilities of 40 LLMs across 21 European languages. The study highlights the importance of considering the impact of different translation services and introduces a multilingual evaluation framework that includes newly created datasets for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how well language models can perform tasks in many European languages, like German, French, Spanish, Italian, and Portuguese. It’s important because language models are getting better at understanding and generating text, but we need to make sure they work well across different languages and cultures. The authors test 40 language models on five big datasets that are translated into multiple European languages. They also look at how different translation services affect the results. |
Keywords
» Artificial intelligence » Translation
