Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation (2026.eacl-long)
Copied to clipboard
Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, Michael Barlow, Gregor Ziegltrum, Volker Stampa, Bastian Harren, Björn Deiseroth
| Challenge: | Recent studies show that data quality can significantly boost performance and training efficiency for large language models. |
| Approach: | They propose a German-language dataset curation pipeline that combines heuristic and model-based filtering techniques with synthetic data generation. |
| Outcome: | The proposed pipeline can be used to create a large-scale German pre-training dataset using common Crawl web data, fineweb2 and synthetically generated data conditioned on real, organic web data. |
Similar Papers
Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls (2025.emnlp-main)
Copied to clipboard
Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-Wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu
| Challenge: | a large-scale empirical study compares natural web data, diverse synthetic types, and mixtures of natural and synthetic data. |
| Approach: | They conduct a large-scale empirical study on large-volume LLMs using a unified protocol and scaling laws. |
| Outcome: | The proposed method is faster than pre-training on natural web data, the authors show . their results are consistent with previous studies on rephrased text and textbooks . |
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
| Approach: | They propose a generic workflow for LLM-driven synthetic data generation. |
| Outcome: | The proposed workflows highlight gaps in existing research and outline avenues for future studies. |
UnitCoder: Scalable Code Synthesis from Pre-training Corpora (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) excel at code understanding and generation, yet code generation remains a challenge. |
| Approach: | They propose a model that supervises pre-training data quality through automatically generated unit tests while ensuring correctness via an iterative fix and refine flow. |
| Outcome: | The proposed model improves performance on a large dataset with high quality pre-training data. |
Scaling Data-Constrained Language Models with Synthetic Data (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) improve with more training data, but practical limitations on data collection constrain further scaling. |
| Approach: | They compare three strategies to generate Japanese text, repeat the limited Japanese Web text, and use English Web text to fill the data shortfall. |
| Outcome: | The proposed model outperforms baselines and achieves the performance achieved when the entire token budget is filled with additional organic Japanese Web text. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
Copied to clipboard
Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have explored using large language models to generate synthetic datasets . however, the effectiveness of the LLM-generated synthetic data is inconsistent across different classification tasks. |
| Approach: | They propose to use large language models to generate synthetic datasets to better understand factors that moderate the effectiveness of LLM-generated synthetic data. |
| Outcome: | The results show that subjectivity is negatively associated with the performance of the model trained on synthetic data. |
On the Impact of Cross-Domain Data on German Language Models (2023.findings-emnlp)
Copied to clipboard
Amin Dada, Aokun Chen, Cheng Peng, Kaleb Smith, Ahmad Idrissi-Yaghir, Constantin Seibold, Jianning Li, Lars Heiliger, Christoph Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu
| Challenge: | Traditionally, large language models have been trained on general web crawls or domain-specific data. |
| Approach: | They present a German dataset and a dataset aimed at containing high-quality data to examine the importance of data diversity over quality. |
| Outcome: | The proposed model outperforms models trained on quality data on multiple downstream tasks. |
Evaluating Language Models as Synthetic Data Generators (2025.acl-long)
Copied to clipboard
Seungone Kim, Juyoung Suk, Xiang Yue, Vijay Viswanathan, Seongyun Lee, Yizhong Wang, Kiril Gashteovski, Carolin Lawrence, Sean Welleck, Graham Neubig
| Challenge: | Prior studies have focused on developing effective data generation methods, but lack systematic comparison of different LMs as data generators in a unified setting. |
| Approach: | They propose to use a benchmark to compare language models' data generation abilities against a set of standardized settings and metrics. |
| Outcome: | The proposed benchmark provides standardized settings and metrics to evaluate LMs’ data generation abilities. |
LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have achieved remarkable success, yet this progress is predominantly centered on English. |
| Approach: | They create two German-only decoder models from scratch and publish them for the (German) NLP research community to use. |
| Outcome: | The two models performed competitively on the German SuperGLEBer benchmark, but performance improvements plateaued early during training, offering valuable insights into resource allocation for future models. |
The Data Frontier for Large Language Models: Selection, Synthesis, and Tools (2026.acl-tutorials)
Copied to clipboard
| Challenge: | acquiring and curating high-quality training data remains a significant bottleneck . acquiring such high-quality data is a key challenge for researchers and practitioners . |
| Approach: | This tutorial provides a comprehensive and practical guide to the state-of-the-art in data research directions for LLMs. |
| Outcome: | The tutorial covers methods for curating the most valuable information from vast, noisy datasets and the synthetic data revolution. |