no code implementations • 21 Feb 2024 • Alexander Arno Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali
The adaption of multilingual pre-trained Large Language Models (LLMs) into eloquent and helpful assistants is essential to facilitate their use across different language regions.
no code implementations • 12 Oct 2023 • Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann, Max Lübbering, Johannes Leveling, Katrin Klug, Jan Ebert, Niclas Doll, Jasper Schulze Buschhoff, Charvi Jain, Alexander Arno Weber, Lena Jurkschat, Hammam Abdelwahab, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Samuel Weinbach, Rafet Sifa, Stefan Kesselheim, Nicolas Flores-Herr
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 advancements in pretraining objectives, leaving tokenizer influence as a blind spot.
no code implementations • 19 May 2022 • Michael Fromm, Max Berrendorf, Johanna Reiml, Isabelle Mayerhofer, Siddharth Bhargava, Evgeniy Faerman, Thomas Seidl
While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal.
no code implementations • 22 Nov 2021 • Jason Jooste, Michael Fromm, Matthias Schubert
Increasing the resolution of this height information also showed little effect.
1 code implementation • EMNLP (insights) 2021 • Nataliia Kees, Michael Fromm, Evgeniy Faerman, Thomas Seidl
High-quality arguments are an essential part of decision-making.
2 code implementations • 10 Dec 2020 • Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.
1 code implementation • 4 Nov 2020 • Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman
In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects.
1 code implementation • 29 Jan 2020 • Diana Davletshina, Valentyn Melnychuk, Viet Tran, Hitansh Singla, Max Berrendorf, Evgeniy Faerman, Michael Fromm, Matthias Schubert
Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
no code implementations • 16 Oct 2019 • Tobias Weber, Dieter Kranzlmüller, Michael Fromm, Nelson Tavares de Sousa
Both applications perform at scale with the proposed models which are available for re-use.
no code implementations • 26 May 2019 • Michael Fromm, Evgeniy Faerman, Thomas Seidl
In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them.