Summary of Evaluating the Elementary Multilingual Capabilities Of Large Language Models with Multiq, by Carolin Holtermann et al.
Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ
by Carolin Holtermann, Paul Röttger, Timm Dill, Anne Lauscher
First submitted to arxiv on: 6 Mar 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 investigates the multilingual capabilities of state-of-the-art open large language models (LLMs) beyond their intended use. Current LLMs are primarily designed for English or a few high-resource languages, but users often prompt them in many different languages. The authors introduce MultiQ, a new benchmark for basic open-ended question answering across 137 languages, and evaluate the language fidelity and question answering accuracy of various LLMs. They find that most models respond faithfully and accurately to some extent beyond their intended use, but there is a long tail of languages where models are neither accurate nor faithful. The authors also explore tokenization as a potential explanation for these findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models work in many different languages, even if they weren’t designed for those languages. Right now, most language models are only good at understanding and generating text in English or a few other languages. But people often try to use them to understand text in lots of other languages too. The authors created a new test that shows how well different language models do on this task. They found that most language models can answer questions correctly, but they’re not always good at understanding the language they’re supposed to be answering in. There are some languages where the models don’t do very well at all. |
Keywords
» Artificial intelligence » Prompt » Question answering » Tokenization
