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Teuken-7B-Base and Teuken-7B-Instruct: Towards European LLMs (2024)

(arxiv.org)
246 points doener | 1 comments | 15 Apr 25 10:17 UTC | HN request time: 0.225s | source
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ozgune ◴[15 Apr 25 12:12 UTC] No.43691597[source]▶
I had a related, but orthogonal question about multilingual LLMs.

When I ask smaller models a question in English, the model does well. When I ask the same model a question in Turkish, the answer is mediocre. When I ask the model to translate my question into English, get the answer, and translate the answer back to Turkish, the model again does well.

For example, I tried the above with Llama 3.3 70B, and asked it to plan me a 3-day trip to Istanbul. When I asked Llama to do the translations between English <> Turkish, the answer was notably better.

Anyone else observed a similar behavior?

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spacebanana7 ◴[15 Apr 25 12:29 UTC] No.43691774[source]▶
I suspect this also happens in programming languages. Subjectively I get the feeling that LLMs prefer to write in Python or JS.

Would be interesting to see whether they actually score better in leetcode questions when using python.

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1. omneity ◴[15 Apr 25 14:09 UTC] No.43692925[source]▶
See my other comment. The answer is transfer learning: leveraging massive amounts of data in one language like Python, a few bridges to another language like Ruby, and obtain a “native” result in the other language.

But in this case the LLM is not exposed to explicit translation pairs between these two languages and rather by seeing enough examples in similar contexts, LLMs transfer some of their learnings in Python to Ruby (for better or worse results)