Papers by Nicolas Flores-Herr
Fact Finder - Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs (2026.eacl-demo)
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Daniel Steinigen, Roman Teucher, Timm Heine Ruland, Max Rudat, Nicolas Flores-Herr, Peter Fischer, Nikola Milosevic, Christopher Schymura, Angelo Ziletti
| Challenge: | Recent advances in Large Language Models have demonstrated their proficiency in answering natural language queries. |
| Approach: | They propose a system that augments Large Language Models with domain-specific knowledge graphs . they evaluate a medical KG and use a KG-based retrieval approach to enhance factual correctness . |
| Outcome: | The proposed system surpasses a standalone LLM in accuracy and completeness on a medical KG dataset. |
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions? (2024.emnlp-main)
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Alexander Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali
| Challenge: | a study of multilingual pre-trained LLMs on parallel instruction-tuning benchmarks shows that instruction-following models can be used across languages by up to 9.9%. |
| Approach: | They conduct an extensive study of the performance of multilingual pre-trained LLMs instruction-tuned on parallel instruction-uning datasets. |
| Outcome: | The proposed model improves cross-lingual instruction following capabilities by 9.9% on a large and mid-sized LLM on parallel instruction-tuning datasets. |
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models (2025.emnlp-main)
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Mehdi Ali, Manuel Brack, Max Lübbering, Elias Wendt, Abbas Goher Khan, Richard Rutmann, Alex Jude, Maurice Kraus, Alexander Arno Weber, Felix Stollenwerk, David Kaczér, Florian Mai, Lucie Flek, Rafet Sifa, Nicolas Flores-Herr, Joachim Koehler, Patrick Schramowski, Michael Fromm, Kristian Kersting
| Challenge: | Existing open-source multilingual datasets rely on heuristic filtering methods restricting both their cross-lingual transferability and scalability. |
| Approach: | They propose a systematic approach that curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. |
| Outcome: | Evaluated empirically across 35 languages, the proposed approach outperforms current heuristic filtering methods like Fineweb2 and improves model training quality and retention rates. |
Tokenizer Choice For LLM Training: Negligible or Crucial? (2024.findings-naacl)
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Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann, Max Lübbering, Johannes Leveling, Katrin Klug, Jan Ebert, Niclas Doll, Jasper Buschhoff, Charvi Jain, Alexander Weber, Lena Jurkschat, Hammam Abdelwahab, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Samuel Weinbach, Rafet Sifa, Stefan Kesselheim, Nicolas Flores-Herr
| Challenge: | Recent success of large language models has been driven by curating the training dataset composition, scaling of model architectures and advancements in pretraining objectives, leaving tokenizer influence as a blind spot. |
| Approach: | They conduct a comprehensive study on the influence of tokenizer choice on LLM downstream performance by training 24 mono- and multilingual LLMs at a 2.6B parameter scale. |
| Outcome: | The proposed model can significantly impact the model's downstream performance and training costs. |