---
title: "Ludwig-Maximilians-Universität München — works"
description: "34 works by researchers of Ludwig-Maximilians-Universität München in our library, the most cited first."
canonical: "https://research.elamur.ai/free/en/universities/ludwig-maximilians-universitat-munchen/works/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/universities/ludwig-maximilians-universitat-munchen/works/)

[home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [universities](https://md.research.elamur.ai/free/en/universities/) / [Ludwig-Maximilians-Universität München](https://md.research.elamur.ai/free/en/universities/ludwig-maximilians-universitat-munchen/) / works

# Works of Ludwig-Maximilians-Universität München

34 works · the most cited first · [← Ludwig-Maximilians-Universität München](https://md.research.elamur.ai/free/en/universities/ludwig-maximilians-universitat-munchen/)

1. [**SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python**Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, et al.*2019 · 38,317 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/)
2. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,429 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
3. [**Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference**Timo Schick, Hinrich Schütze*2021 · 1,796 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/)
4. [**It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners**Timo Schick, Hinrich Schütze*2021 · 1,118 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/)
5. [**ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs**Wenpeng Yin, Hinrich Schütze, Bing Xiang, et al.*2016 · 1,048 citations*](https://md.research.elamur.ai/free/en/papers/yin-2016-abcnn-attention-based-convolutional-neural/)
6. [**Findings of the 2016 Conference on Machine Translation**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2016 · 936 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2016-findings-2016-conference-machine-translation/)
7. [**Findings of the 2019 Conference on Machine Translation (WMT19)**Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, et al.*2019 · 839 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2019-findings-2019-conference-machine-translation/)
8. [**Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly**Nora Kassner, Hinrich Schütze*2020 · 431 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2020-negated-misprimed-probes-pretrained-language/)
9. [**The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation**Barbara Plank*2022 · 364 citations*](https://md.research.elamur.ai/free/en/papers/plank-2022-problem-human-label-variation-ground/)
10. [**Generating Datasets with Pretrained Language Models**Timo Schick, Hinrich Schutze*2021 · 250 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/)
11. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 207 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
12. [**Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models**Nora Kassner, Philipp Dufter, Hinrich Schütze*2021 · 175 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2021-multilingual-lama-investigating-knowledge-multilingual/)
13. [**Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change**Dominik Schlechtweg, Sabine Schulte im Walde, Stefanie Eckmann*2018 · 158 citations*](https://md.research.elamur.ai/free/en/papers/schlechtweg-2018-diachronic-usage-relatedness-durel-framework/)
14. [**FINDINGS OF THE IWSLT 2023 EVALUATION CAMPAIGN**Milind Agarwal, Sweta Agrawal, Antonios Anastasopoulos, et al.*2023 · 153 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-findings-iwslt-2023-evaluation-campaign/)
15. [**Investigating Pretrained Language Models for Graph-to-Text Generation**Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, et al.*2021 · 149 citations*](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/)
16. [**GlotLID: Language Identification for Low-Resource Languages**Amir Hossein Kargaran, Ayyoob Imani, François Yvon, et al.*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/kargaran-2023-glotlid-language-identification-low-resource/)
17. [**Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words**Valentin, Hofmann*2021 · 101 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-2021-superbizarre-derivational/)
18. [**Are Pretrained Language Models Symbolic Reasoners over Knowledge?**Nora Kassner, Benno Krojer, Hinrich Schütze*2020 · 79 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2020-are-pretrained-language-models-symbolic/)
19. [**VariErr NLI: Separating Annotation Error from Human Label Variation**Leon Weber-Genzel, Siyao Peng, Marie-Catherine De Marneffe, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/genzel-2024-varierr-nli-separating-annotation-error/)
20. [**Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model**Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban*2023 · 47 citations*](https://md.research.elamur.ai/free/en/papers/weissweiler-2023-chatgpt-wugs-morphology/)
21. [**Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments**Dario Stojanovski, Alexander Fraser*2018 · 46 citations*](https://md.research.elamur.ai/free/en/papers/stojanovski-2018-coreference-coherence-neural-machine-translation/)
22. [**eRST: A Signaled Graph Theory of Discourse Relations and Organization**Amir Zeldes, Tatsuya Aoyama, Yang Janet Liu, et al.*2025 · 31 citations*](https://md.research.elamur.ai/free/en/papers/zeldes-2025-erst-signaled-graph-theory-discourse/)
23. [**SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference**Martin Schmitt, Hinrich Schütze*2019 · 28 citations*](https://md.research.elamur.ai/free/en/papers/schmitt-2019-sherliic-typed-event-focused-lexical/)
24. [**Annotating tense, mood and voice for English, French and German**Anita Ramm, Sharid Loáiciga, Annemarie Friedrich, et al.*2017 · 25 citations*](https://md.research.elamur.ai/free/en/papers/ramm-2017-annotating-tense-mood-voice-english/)
25. [**A Survey of Corpora for Germanic Low-Resource Languages and Dialects**Verena Blaschke, Hinrich Schuetze, Barbara Plank*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/blaschke-2023-survey-corpora-germanic-low-resource/)
26. [**Generalizable Sarcasm Detection is Just Around the Corner, of Course!**Hyewon Jang, Diego Frassinelli*2024 · 22 citations*](https://md.research.elamur.ai/free/en/papers/jang-2024-generalizable-sarcasm-detection-is-just/)
27. [**Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis**Daniela Teodorescu, Saif Mohammad*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/teodorescu-2023-evaluating-emotion-arcs-across-languages/)
28. [**When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs)**Reto Gubelmann, Aikaterini-lida Kalouli, Christina Niklaus, et al.*2023 · 15 citations*](https://md.research.elamur.ai/free/en/papers/gubelmann-2023-when-truth-matters-addressing-pragmatic/)
29. [**A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI**Beiduo Chen, Siyao Peng, Anna Korhonen, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-rose-any-other-name-llm/)
30. [**GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains**Yang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-gdtb-genre-diverse-data-english/)
31. [**Loss and preservation of case in Germanic non-standard varieties**Raffaela Baechler, Simon Pröll*2018 · 4 citations*](https://md.research.elamur.ai/free/en/papers/baechler-2018-loss-preservation-case-germanic-non/)
32. [**The Ethiopic Homily on Holy Easter attributed to John Theologos and its Arabic Vorlage (CPG 4163.2)**Aaron Michael Butts, Ted Erho*2024*](https://md.research.elamur.ai/free/en/papers/butts-2024-ethiopic-homily-holy-easter-attributed/)
33. [**An Ethiopic 'Homily on Peter' attributed to Ephrem**Aaron Michael Butts, Ted Erho, Jonas Karlsson*2025*](https://md.research.elamur.ai/free/en/papers/butts-2025-ethiopic-homily-peter-attributed-ephrem/)
34. [**Exploring Cognitively Informed Sentence Simplification with Gaze-Guided Text Generation**Andreas Säuberli, Diego Frassinelli, Barbara Plank*2026*](https://md.research.elamur.ai/free/en/papers/sauberli-2026-exploring-cognitively-informed-sentence-simplification/)
- [← back to](https://md.research.elamur.ai/free/en/universities/ludwig-maximilians-universitat-munchen/) — Ludwig-Maximilians-Universität München