---
title: "Who cites Berlin Chen — 470 papers of our library"
description: "470 papers of the Elamur Research library cite the works of Berlin Chen, found in their bibliographies; the most cited first."
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---
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# Who cites Berlin Chen

471 citations of their works from 470 papers of our library (of the 141,840 whose bibliographies we read) · 63 with a page here, the most cited first · [← Berlin Chen](https://md.research.elamur.ai/free/en/authors/berlin-chen/)

1. **[Language Models as Knowledge Bases?](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.*2019 · 2,992 citations*
2. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,190 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
3. **[BERT Rediscovers the Classical NLP Pipeline](https://md.research.elamur.ai/free/en/papers/tenney-2019-bert-rediscovers-classical-nlp-pipeline/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ian Tenney, Dipanjan Das, Ellie Pavlick*2019 · 2,002 citations*
4. **[What Does BERT Learn about the Structure of Language?](https://md.research.elamur.ai/free/en/papers/jawahar-2019-what-does-bert-learn-about/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ganesh Jawahar, Benoît Sagot, Djamé Seddah*2019 · 1,799 citations*
5. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,753 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
6. **[How Can We Know What Language Models Know?](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zhengbao Jiang, Frank F. Xu, Jun Araki, et al.*2020 · 1,501 citations*
7. **[Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data](https://md.research.elamur.ai/free/en/papers/bender-2020-climbing-nlu-meaning-form-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Emily M. Bender, Alexander Koller*2020 · 1,375 citations*
8. **[A Structural Probe for Finding Syntax in Word Representations](https://md.research.elamur.ai/free/en/papers/hewitt-2019-structural-probe-finding-syntax-word/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**John Hewitt, Christopher D. Manning*2019 · 1,232 citations*
9. **[Linguistic Knowledge and Transferability of Contextual Representations](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al.*2019 · 1,015 citations*
10. **[MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://md.research.elamur.ai/free/en/papers/zhao-2019-moverscore-text-generation-evaluating-contextualized/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Zhao, Maxime Peyrard, Fei Liu, et al.*2019 · 829 citations*
11. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 785 citations*
12. **[PAWS: Paraphrase Adversaries from Word Scrambling](https://md.research.elamur.ai/free/en/papers/zhang-2019-paws-paraphrase-adversaries-word-scrambling/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yuan Zhang, Jason Baldridge, Luheng He*2019 · 587 citations*
13. **[Probing Classifiers: Promises, Shortcomings, and Advances](https://md.research.elamur.ai/free/en/papers/belinkov-2022-probing-classifiers-promises-shortcomings-advances/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yonatan Belinkov*2022 · 514 citations*
14. **[oLMpics — On what Language Model Pre-training Captures](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 435 citations*
15. **[Factual Probing Is MASK: Learning vs. Learning to Recall](https://md.research.elamur.ai/free/en/papers/zhong-2021-factual-probing-is-mask-learning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zexuan Zhong, Dan Friedman, Danqi Chen*2021 · 411 citations*
16. [**Open Sesame: Getting inside BERT's Linguistic Knowledge Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP**Yongjie Lin, Yi Chern Tan, Robert Frank*2019 · 382 citations*](https://md.research.elamur.ai/free/en/papers/lin-2019-open-sesame-getting-inside-bert/)
17. [**The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models arXiv:2103.06678**Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, et al.*2021 · 365 citations*](https://md.research.elamur.ai/free/en/papers/inoue-2021-interplay-variant-size-task-type/)
18. [**Revisiting Few-sample BERT Fine-tuning arXiv:2006.05987**Tianyi Zhang, Felix Wu, Arzoo Katiyar, et al.*2020 · 362 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-revisiting-few-sample-bert-fine/)
19. **[TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://md.research.elamur.ai/free/en/papers/wu-2020-tod-bert-pre-trained-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chien-Sheng Wu, Steven C.H. Hoi, Richard Socher, et al.*2020 · 361 citations*
20. [**Visualizing and Measuring the Geometry of BERT arXiv:1906.02715**Andy Coenen, Emily Reif, Ann Yuan, et al.*2019 · 343 citations*](https://md.research.elamur.ai/free/en/papers/coenen-2019-visualizing-measuring-geometry-bert/)
21. **[Information-Theoretic Probing with Minimum Description Length](https://md.research.elamur.ai/free/en/papers/voita-2020-information-theoretic-probing-minimum-description/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Elena Voita, Ivan Titov*2020 · 340 citations*
22. **[Information-Theoretic Probing for Linguistic Structure](https://md.research.elamur.ai/free/en/papers/pimentel-2020-information-theoretic-probing-linguistic-structure/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, et al.*2020 · 293 citations*
23. **[Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://md.research.elamur.ai/free/en/papers/pruksachatkun-2020-intermediate-task-transfer-learning-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.*2020 · 281 citations*
24. **[Multi-Sentence Argument Linking](https://md.research.elamur.ai/free/en/papers/ebner-2020-multi-sentence-argument-linking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Seth Ebner, Patrick Xia, Ryan Culkin, et al.*2020 · 281 citations*
25. **[Text Classification Using Label Names Only: A Language Model Self-Training Approach](https://md.research.elamur.ai/free/en/papers/meng-2020-text-classification-label-names-only/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yu Meng, Yunyi Zhang, Jiaxin Huang, et al.*2020 · 274 citations*
26. **[Attention is Not Only a Weight: Analyzing Transformers with Vector Norms](https://md.research.elamur.ai/free/en/papers/kobayashi-2020-attention-is-not-only-weight/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, et al.*2020 · 268 citations*
27. [**Measuring and Reducing Gendered Correlations in Pre-trained Models arXiv:2010.06032**Kellie Webster, Xuezhi Wang, Ian Tenney, et al.*2020 · 253 citations*](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
28. **[Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rishi Bommasani, Kelly Davis, Claire Cardie*2020 · 248 citations*
29. **[Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT](https://md.research.elamur.ai/free/en/papers/wu-2020-perturbed-masking-parameter-free-probing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyong Wu, Yun Chen, Ben Kao, et al.*2020 · 245 citations*
30. **[ZeroGen: Efficient Zero-shot Learning via Dataset Generation](https://md.research.elamur.ai/free/en/papers/ye-2022-zerogen-efficient-zero-shot-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiacheng Ye, Jiahui Gao, Qintong Li, et al.*2022 · 242 citations*
31. [**How Context Affects Language Models' Factual Predictions arXiv:2005.04611**Fabio Petroni, Patrick Lewis, Aleksandra Piktus, et al.*2020 · 231 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-context-affects-language-models-factual/)
32. **[The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives](https://md.research.elamur.ai/free/en/papers/voita-2019-bottom-up-evolution-representations-transformer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Elena Voita, Rico Sennrich, Ivan Titov*2019 · 223 citations*
33. **[Visualizing and Understanding the Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/hao-2019-visualizing-understanding-effectiveness-bert/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaru Hao, Li Dong, Furu Wei, et al.*2019 · 221 citations*
34. **[What Happens To BERT Embeddings During Fine-tuning?](https://md.research.elamur.ai/free/en/papers/merchant-2020-what-happens-bert-embeddings-during/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, et al.*2020 · 218 citations*
35. **[Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al.*2020 · 217 citations*
36. [**What Artificial Neural Networks Can Tell Us About Human Language Acquisition arXiv:2208.07998**Alex Warstadt, Samuel R. Bowman*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2022-artificial-neural-networks-tell-us/)
37. **[Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs](https://md.research.elamur.ai/free/en/papers/warstadt-2019-investigating-bert-s-knowledge-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alex Warstadt, Yu Cao, Ioana Grosu, et al.*2019 · 176 citations*
38. **[Investigating Multilingual NMT Representations at Scale](https://md.research.elamur.ai/free/en/papers/kudugunta-2019-investigating-multilingual-nmt-representations-scale/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sneha Kudugunta, Ankur Bapna, Isaac Caswell, et al.*2019 · 175 citations*
39. **[Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Najoung Kim, Roma Patel, Adam Poliak, et al.*2019 · 161 citations*
40. **[Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://md.research.elamur.ai/free/en/papers/ravichander-2021-probing-probing-paradigm-does-probing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Abhilasha Ravichander, Yonatan Belinkov, Eduard Hovy*2021 · 141 citations*
41. [**SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning**Roshanak Mirzaee, Hossein Rajaby Faghihi, Qiang Ning, et al.*2021 · 93 citations*](https://md.research.elamur.ai/free/en/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/)
42. **[speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://md.research.elamur.ai/free/en/papers/zhang-2021-speechocean762-open-source-non-native/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Junbo Zhang, Zhiwen Zhang, Yongqing Wang, et al.*2021 · 90 citations*
43. **[Coreference-Aware Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/liu-2021-coreference-aware-dialogue-summarization/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Zhengyuan Liu, Ke Shi, Nancy Chen*2021 · 81 citations*
44. **[CLiMP: A Benchmark for Chinese Language Model Evaluation](https://md.research.elamur.ai/free/en/papers/xiang-2021-climp-benchmark-chinese-language-model/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Beilei Xiang, Changbing Yang, Yu Li, et al.*2021 · 73 citations*
45. **[Mission: Impossible Language Models](https://md.research.elamur.ai/free/en/papers/kallini-2024-mission-impossible-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Julie Kallini, Isabel Papadimitriou, Richard Futrell, et al.*2024 · 69 citations*
46. **[A Fine-Grained Analysis of BERTScore](https://md.research.elamur.ai/free/en/papers/hanna-2021-fine-grained-analysis-bertscore/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Michael Hanna, Ondřej Bojar*2021 · 62 citations*
47. [**A Neural Entity Coreference Resolution Review Expert Systems with Applications, Volume 168, 15 April 2021, 114,466**Nikolaos Stylianou, Ioannis Vlahavas*2019 · 48 citations*](https://md.research.elamur.ai/free/en/papers/stylianou-2019-neural-entity-coreference-resolution-review/)
48. **[CxGBERT: BERT meets Construction Grammar](https://md.research.elamur.ai/free/en/papers/madabushi-2020-cxgbert-bert-meets-construction-grammar/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Harish Tayyar Madabushi, Laurence Romain, Dagmar Divjak, et al.*2020 · 46 citations*
49. **[Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks](https://md.research.elamur.ai/free/en/papers/sun-2021-cross-cultural-similarity-features-cross/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jimin Sun, Hwijeen Ahn, Chan Young Park, et al.*2021 · 28 citations*
50. [**BLiMP: The Benchmark of Linguistic Minimal Pairs for English**Alex Warstadt, Alicia Parrish, Haokun Liu, et al.*2020 · 21 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2020-blimp-benchmark-linguistic-minimal-pairs/)
51. **[Towards Robust Mispronunciation Detection and Diagnosis for L2 English Learners with Accent-Modulating Methods](https://md.research.elamur.ai/free/en/papers/jiang-2021-towards-robust-mispronunciation-detection-diagnosis/) [IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)](https://md.research.elamur.ai/free/en/venues/ieee-automatic-speech-recognition-and-understanding-workshop-asru/)**Shao-Wei Fan Jiang, Bi-Cheng Yan, Tien-Hong Lo, et al.*2021 · 20 citations*
52. [**Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary**Stefan Grünewald, Annemarie Friedrich, Jonas Kuhn*2021 · 18 citations*](https://md.research.elamur.ai/free/en/papers/grunewald-2021-applying-occam-s-razor-transformer/)
53. [**Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?**Avi Shmidman, Cheyn Shmuel Shmidman, Dan Bareket, et al.*2023 · 5 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2023-do-pretrained-contextual-language-models/)
54. **[Predicting the Argumenthood of English Prepositional Phrases](https://md.research.elamur.ai/free/en/papers/kim-2018-predicting-argumenthood-english-prepositional-phrases/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Najoung Kim, Kyle Rawlins, Benjamin Van Durme, et al.*2018 · 4 citations*
55. **[On Quick Kisses and How to Make Them Count: A Study on Event Construal in Light Verb Constructions with BERT](https://md.research.elamur.ai/free/en/papers/liu-2023-quick-kisses-how-make-them/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Chenxin Liu, Emmanuele Chersoni*2023 · 2 citations*
56. **[Examining Political Rhetoric with Epistemic Stance Detection](https://md.research.elamur.ai/free/en/papers/gupta-2022-examining-political-rhetoric-epistemic-stance/) [Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)](https://md.research.elamur.ai/free/en/venues/nlpcss/)**Ankita Gupta, Su Lin Blodgett, Justin H Gross, et al.*2022 · 2 citations*
57. [**Automated detection of pronunciation errors in non-native English speech employing deep learning arXiv:2209.06265**Daniel Korzekwa*2022 · 1 citation*](https://md.research.elamur.ai/free/en/papers/korzekwa-2022-automated-detection-pronunciation-errors-non/)
58. [**A Fusion-Aware Two-Stage Framework for Mispronunciation Detection and Diagnosis in Low-Resource Modern Standard Arabic arXiv:2606.24086**Jing Yang, Shuqing Zhang, Yongyi Deng, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yang-2026-fusion-aware-two-stage-framework/)
59. **[Compositional Meaning Representations in LLMs: a Critical Review of Probing Studies](https://md.research.elamur.ai/free/en/papers/marro-2026-compositional-meaning-representations-llms-critical/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Rémy Marro*2026*
60. **[Predicting States of Understanding in Explanatory Interactions Using Cognitive Load-Related Linguistic Cues](https://md.research.elamur.ai/free/en/papers/wang-2026-predicting-states-understanding-explanatory-interactions/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yu Wang, Olcay Türk, Angela Grimminger, et al.*2026*
61. **[Sequence Labeling for Constituent Parsing: A Comparative Study and Encoding Innovations](https://md.research.elamur.ai/free/en/papers/roca-2026-sequence-labeling-constituent-parsing-comparative/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Diego Roca, David Vilares, Carlos Gómez-Rodríguez*2026*
62. **[Transformer Attention as a Unified Model of Encoding and Retrieval in Human Sentence Processing](https://md.research.elamur.ai/free/en/papers/parker-2026-transformer-attention-as-unified-model/) [Workshop on Cognitive Modeling and Computational Linguistics (CMCL)](https://md.research.elamur.ai/free/en/venues/cmcl/)**Dan Parker*2026*
63. **[Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/anikina-2025-reverse-probing-evaluating-knowledge-transfer/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Tatiana Anikina, Arne Binder, David Harbecke, et al.*2025*

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