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

(arxiv.org)
248 points doener | 1 comments | 15 Apr 25 10:17 UTC | HN request time: 0.302s | 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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1. dingdingdang ◴[15 Apr 25 21:33 UTC] No.43698667[source]▶
Indeed. I've thought from the beginning that LLMs should focus specifically on ONE language for this exact reason (i.e. mediocre/bad duplication of data in multiple languages). All other languages than English essentially "syphon" off capacity/layers/weights that could otherwise have held more genuine data/knowledge. Other languages should not come into the picture afaics - dedicated translation LLMs/existing-solutions can handle this aspect just fine and there's just no salient reason to fold partial-multi-language-capacity in through fuzzy/unorganised training.