Summary of Teuken-7b-base & Teuken-7b-instruct: Towards European Llms, by Mehdi Ali et al.

Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs

by Mehdi Ali, Michael Fromm, Klaudia Thellmann, Jan Ebert, Alexander Arno Weber, Richard Rutmann, Charvi Jain, Max Lübbering, Daniel Steinigen, Johannes Leveling, Katrin Klug, Jasper Schulze Buschhoff, Lena Jurkschat, Hammam Abdelwahab, Benny Jörg Stein, Karl-Heinz Sylla, Pavel Denisov, Nicolo’ Brandizzi, Qasid Saleem, Anirban Bhowmick, Lennard Helmer, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Alex Jude, Lalith Manjunath, Samuel Weinbach, Carolin Penke, Oleg Filatov, Shima Asaadi, Fabio Barth, Rafet Sifa, Fabian Küch, Andreas Herten, René Jäkel, Georg Rehm, Stefan Kesselheim, Joachim Köhler, Nicolas Flores-Herr

First submitted to arxiv on: 30 Sep 2024

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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 In this paper, researchers introduce two new multilingual language models designed to support all 24 official languages of the European Union. These models are trained on a dataset with a significant portion (around 60%) of non-English data, using a custom tokenizer optimized for multilingual processing. The development principles include data composition, tokenizer optimization, and training methodologies. The models demonstrate competitive performance across various multilingual benchmarks, including ARC, HellaSwag, MMLU, and TruthfulQA.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper presents two new language models that can understand and generate text in all 24 official languages of the European Union. These models are special because they’re trained on a lot of data that isn’t English, which helps them be more diverse and helpful for people who don’t speak English. The researchers explain how they built these models, including what kind of data they used and how they optimized their processing to work with many languages. The models perform well on tests comparing language models across different languages.