Michael Fromm

Ludwig-Maximilians-Universität München

H-index: 5

Europe-Germany

About Michael Fromm

Michael Fromm, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at Ludwig-Maximilians-Universität München, specializes in the field of Machine Learning, Large Language Models, Argument Mining.

His recent articles reflect a diverse array of research interests and contributions to the field:

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

Tokenizer Choice For LLM Training: Negligible or Crucial?

Cross-domain argument quality estimation

Towards a holistic view on argument quality prediction

Machine learning driven argument mining

Conifer Seedling Detection in UAV-Imagery with RGB-Depth Information

Argument Mining Driven Analysis of Peer-Reviews

Active Learning for Argument Strength Estimation

Michael Fromm Information

University	Ludwig-Maximilians-Universität München
Position	___
Citations(all)	185
Citations(since 2020)	185
Cited By	40
hIndex(all)	5
hIndex(since 2020)	5
i10Index(all)	5
i10Index(since 2020)	5
Email	Access Email
University Profile Page	Ludwig-Maximilians-Universität München
Google Scholar	View Google Scholar Profile

Michael Fromm Skills & Research Interests

Machine Learning

Large Language Models

Argument Mining

Top articles of Michael Fromm

Title	Journal	Author(s)	Publication Date
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?	arXiv preprint arXiv:2402.13703	Alexander Arno Weber Klaudia Thellmann Jan Ebert Nicolas Flores-Herr Jens Lehmann ...	2024/2/21
Tokenizer Choice For LLM Training: Negligible or Crucial?	arXiv preprint arXiv:2310.08754	Mehdi Ali Michael Fromm Klaudia Thellmann Richard Rutmann Max Lübbering ...	2023/10/12
Cross-domain argument quality estimation		Michael Fromm Max Berrendorf Evgeniy Faerman Thomas Seidl	2023/7
Towards a holistic view on argument quality prediction	arXiv preprint arXiv:2205.09803	Michael Fromm Max Berrendorf Johanna Reiml Isabelle Mayerhofer Siddharth Bhargava ...	2022/5/19
Machine learning driven argument mining		Michael Fromm	2022/7/29
Conifer Seedling Detection in UAV-Imagery with RGB-Depth Information	arXiv preprint arXiv:2111.11388	Jason Jooste Michael Fromm Matthias Schubert	2021/11/22
Argument Mining Driven Analysis of Peer-Reviews	Proceedings of the AAAI Conference on Artificial Intelligence	Michael Fromm Evgeniy Faerman Max Berrendorf Siddharth Bhargava Ruoxia Qi ...	2021/5/18
Active Learning for Argument Strength Estimation	arXiv preprint arXiv:2109.11319	Nataliia Kees Michael Fromm Evgeniy Faerman Thomas Seidl	2021/9/23
Diversity Aware Relevance Learning for Argument Search		Michael Fromm Max Berrendorf Sandra Obermeier Thomas Seidl Evgeniy Faerman	2021
Relational and Fine-Grained Argument Mining: The LMU Munich project ReMLAV within the DFG Priority Program RATIO “Robust Argumentation Machines”	Datenbank-Spektrum	Dietrich Trautmann Michael Fromm Volker Tresp Thomas Seidl Hinrich Schütze	2020/7
Using supervised learning to classify metadata of research data by field of study	Quantitative Science Studies	Tobias Weber Dieter Kranzlmüller Michael Fromm Nelson Tavares de Sousa	2020/6/1
Unsupervised anomaly detection for X-ray images	arXiv preprint arXiv:2001.10883	Diana Davletshina Valentyn Melnychuk Viet Tran Hitansh Singla Max Berrendorf ...	2020/1/29