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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: 0.233s | 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. omneity ◴[15 Apr 25 14:05 UTC] No.43692874[source]▶
For most low-resource languages, support in LLMs is trained through translation pairs between english and the other languages, because translation data is easier to come across than say, conversations about coding, history, physics, basically the kind of data that is usually used for instruct training.

This kind of training data typically looks like ChatGPT style conversations where all the prompts are all templated like “Translate the following text from X to Y: [text]” and the LLM’s expected answer is the translated text.

LLMs can generalize through transfer learning (to a certain extent) from these translation pairs to some understanding (strong) and even answering (weak) in the target language. It also means that the LLM’s actual sweet spot is in translation itself since that’s what was trained in, not just a generalization.