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2020 – today
- 2025
[c13]Esteban Garces Arias, Meimingwei Li, Christian Heumann, Matthias Aßenmacher:
Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation. COLING 2025: 9992-10020- 2024
- [c12]Esteban Garces Arias, Julian Rodemann, Meimingwei Li, Christian Heumann, Matthias Aßenmacher:
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation. EMNLP (Findings) 2024: 15060-15080 - [c11]Björn Deiseroth, Max Meuer, Nikolas Gritsch, Constantin Eichenberg, Patrick Schramowski, Matthias Aßenmacher, Kristian Kersting:
Divergent Token Metrics: Measuring degradation to prune away LLM components - and optimize quantization. NAACL-HLT 2024: 6764-6783 - [i19]Esteban Garces Arias, Julian Rodemann, Meimingwei Li, Christian Heumann, Matthias Aßenmacher:
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation. CoRR abs/2407.18698 (2024) - [i18]Luis Mayer, Christian Heumann, Matthias Aßenmacher:
Can OpenSource beat ChatGPT? - A Comparative Study of Large Language Models for Text-to-Code Generation. CoRR abs/2409.04164 (2024) - [i17]Andreas Stephan, Dawei Zhu, Matthias Aßenmacher, Xiaoyu Shen, Benjamin Roth:
From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning Tasks. CoRR abs/2409.04168 (2024) - [i16]Alexander Wuttke, Matthias Aßenmacher, Christopher Klamm, Max M. Lang, Quirin Würschinger, Frauke Kreuter:
AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers. CoRR abs/2410.01824 (2024) - [i15]Esteban Garces Arias, Meimingwei Li, Christian Heumann, Matthias Aßenmacher:
Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation. CoRR abs/2410.06097 (2024) - [i14]Esteban Garces Arias, Hannah Blocher, Julian Rodemann, Meimingwei Li, Christian Heumann, Matthias Aßenmacher:
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework. CoRR abs/2410.18653 (2024) - [i13]Bolei Ma, Berk Yoztyurk, Anna-Carolina Haensch, Xinpeng Wang, Markus Herklotz, Frauke Kreuter, Barbara Plank, Matthias Aßenmacher:
Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study. CoRR abs/2412.13169 (2024) - 2023
- [c10]Esteban Garces Arias, Vallari Pai, Matthias Schöffel, Christian Heumann, Matthias Aßenmacher:
Automatic Transcription of Handwritten Old Occitan Language. EMNLP 2023: 15416-15439 - [c9]Matthias Aßenmacher, Lukas Rauch, Jann Goschenhofer, Andreas Stephan, Bernd Bischl, Benjamin Roth, Bernhard Sick:
Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. IAL@PKDD/ECML 2023: 65-73 - [c8]Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick:
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. ECML/PKDD (1) 2023: 55-74 - [c7]Philipp Koch, Gilary Vera Nuñez, Esteban Garces Arias, Christian Heumann, Matthias Schöffel, Alexander Häberlin, Matthias Aßenmacher:
A tailored Handwritten-Text-Recognition System for Medieval Latin. ALP@RANLP 2023: 103-110 - [i12]Cem Akkus, Luyang Chu, Vladana Djakovic, Steffen Jauch-Walser, Philipp Koch, Giacomo Loss, Christopher Marquardt, Marco Moldovan, Nadja Sauter, Maximilian Schneider, Rickmer Schulte, Karol Urbanczyk, Jann Goschenhofer, Christian Heumann, Rasmus Hvingelby, Daniel Schalk, Matthias Aßenmacher:
Multimodal Deep Learning. CoRR abs/2301.04856 (2023) - [i11]Lukas Rauch, Matthias Aßenmacher
, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick:
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. CoRR abs/2306.10087 (2023) - [i10]Ibrahim Tolga Öztürk, Rostislav Nedelchev, Christian Heumann, Esteban Garces Arias, Marius Roger, Bernd Bischl, Matthias Aßenmacher:
How Different Is Stereotypical Bias Across Languages? CoRR abs/2307.07331 (2023) - [i9]Matthias Aßenmacher, Nadja Sauter, Christian Heumann:
Classifying multilingual party manifestos: Domain transfer across country, time, and genre. CoRR abs/2307.16511 (2023) - [i8]Philipp Koch, Gilary Vera Nuñez, Esteban Garces Arias, Christian Heumann, Matthias Schöffel, Alexander Häberlin, Matthias Aßenmacher:
A tailored Handwritten-Text-Recognition System for Medieval Latin. CoRR abs/2308.09368 (2023) - [i7]Stefanie Urchs, Veronika Thurner, Matthias Aßenmacher, Christian Heumann, Stephanie Thiemichen:
How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses. CoRR abs/2310.03031 (2023) - [i6]Björn Deiseroth, Max Meuer, Nikolas Gritsch, Constantin Eichenberg, Patrick Schramowski, Matthias Aßenmacher, Kristian Kersting:
Divergent Token Metrics: Measuring degradation to prune away LLM components - and optimize quantization. CoRR abs/2311.01544 (2023) - 2022
- [c6]Philipp Koch, Matthias Aßenmacher, Christian Heumann:
Pre-trained language models evaluating themselves - A comparative study. Insights@ACL 2022: 180-187 - [c5]Elisabeth Lebmeier, Matthias Aßenmacher, Christian Heumann:
On the Current State of Reproducibility and Reporting of Uncertainty for Aspect-Based Sentiment Analysis. ECML/PKDD (2) 2022: 537-552 - 2021
- [b1]Matthias Aßenmacher:
Comparability, evaluation and benchmarking of large pre-trained language models. Ludwig Maximilian University of Munich, Germany, 2021 - [c4]Maximilian Meidinger, Matthias Aßenmacher:
A New Benchmark for NLP in Social Sciences: Evaluating the Usefulness of Pre-trained Language Models for Classifying Open-ended Survey Responses. ICAART (2) 2021: 866-873 - [c3]Matthias Aßenmacher, Alessandra Corvonato, Christian Heumann:
Re-Evaluating GermEval17 Using German Pre-Trained Language Models. SwissText 2021 - [i5]Matthias Aßenmacher, Alessandra Corvonato, Christian Heumann:
Re-Evaluating GermEval17 Using German Pre-Trained Language Models. CoRR abs/2102.12330 (2021) - [i4]Patrick Schulze, Simon Wiegrebe, Paul W. Thurner, Christian Heumann, Matthias Aßenmacher, Sandra Wankmüller:
Exploring Topic-Metadata Relationships with the STM: A Bayesian Approach. CoRR abs/2104.02496 (2021) - [i3]Matthias Aßenmacher, Patrick Schulze, Christian Heumann:
Benchmarking down-scaled (not so large) pre-trained language models. CoRR abs/2105.04876 (2021) - 2020
- [c2]Maike Guderlei, Matthias Aßenmacher:
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News. COLING 2020: 6339-6349 - [c1]Matthias Aßenmacher, Christian Heumann:
On the Comparability of Pre-trained Language Models. SwissText/KONVENS 2020 - [i2]Matthias Aßenmacher, Christian Heumann:
On the comparability of Pre-trained Language Models. CoRR abs/2001.00781 (2020) - [i1]V. D. Viellieber, Matthias Aßenmacher:
Pre-trained language models as knowledge bases for Automotive Complaint Analysis. CoRR abs/2012.02558 (2020)
Coauthor Index
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last updated on 2025-01-24 18:10 CET by the dblp team
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