activity
20232026
most citedTokenizer Choice For LLM Training: Negligible or Crucial?

4 citations · 10 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CL2026

KletterMix: Climbing Toward High-Quality German Pretraining Data - The Full Report

Maurice Kraus, Ruben Härle, Sebastian Sztwiertnia +5

High-quality pretraining data is a central ingredient in modern language models, but German-language resources remain far less developed than their English counterparts: they are o…

cs.CL2026★ 1 cited

A Sovereign, Open-Source Foundation Model for German and English

Soofi-Team, :, Benedikt Droste +30

We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English. Its hybrid design activates only 3B…

cs.LG2026

Modalities, a PyTorch-native Framework For Large-scale LLM Training and Research

Max Lübbering, Timm Ruland, Richard Rutmann +8

Today's LLM (pre-) training and research workflows typically allocate a significant amount of compute to large-scale ablation studies. Despite the substantial compute costs of thes…

cs.CL2025

Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models

Mehdi Ali, Manuel Brack, Max Lübbering +15

High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets r…

cs.CL2024★ 3 cited

Towards Multilingual LLM Evaluation for European Languages

Klaudia Thellmann, Bernhard Stadler, Michael Fromm +8

The rise of Large Language Models (LLMs) has revolutionized natural language processing across numerous languages and tasks. However, evaluating LLM performance in a consistent and…

cs.CL2024

Data Processing for the OpenGPT-X Model Family

Nicolo' Brandizzi, Hammam Abdelwahab, Anirban Bhowmick +19

This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performa…

cs.CL2024★ 2 cited

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

Mehdi Ali, Michael Fromm, Klaudia Thellmann +38

We present two multilingual LLMs, Teuken 7B-base and Teuken 7B-instruct, designed to embrace Europe's linguistic diversity by supporting all 24 official languages of the European U…

cs.CL2024

Do Multilingual Large Language Models Mitigate Stereotype Bias?

Shangrui Nie, Michael Fromm, Charles Welch +7

While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training o…

cs.CL2024

Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?

Alexander Arno Weber, Klaudia Thellmann, Jan Ebert +4

The adaption of multilingual pre-trained LLMs into eloquent and helpful assistants is essential to facilitate their use across different language regions. In that spirit, we are th…

cs.LG2023★ 4 cited

Tokenizer Choice For LLM Training: Negligible or Crucial?

Mehdi Ali, Michael Fromm, Klaudia Thellmann +18

The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and…