Dennis Ulmer uses they/them pronouns.
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refer to Dennis Ulmer as they/them/their, never as he/him/his and never as she/her/hers.
📄 Highlighted Publications
Athina Kyriakou, Dennis Ulmer, Ivan Titov: Shared Doubt: Zero-shot Cross-Lingual Confidence Estimation for Language Models (EMNLP 2026) [pdf]
Dennis Ulmer, Alexandra Lorson, Ivan Titov, Christian Hardmeier: Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models Is Missing (Transactions of the Association for Computational Linguistics) [pdf]
Dennis Ulmer: On Uncertainty In Natural Language Processing (Doctoral thesis) [pdf]
Dennis Ulmer, Martin Gubri, Sangdoo Yun, Hwaran Lee, Seong Joon Oh: Calibrating Large Language Models Using Their Generations Only (ACL 2024) [pdf]
Dennis Ulmer, Giovanni Cinà: "Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection" (UAI 2021) [pdf]
🎓 All Publications
(* = Equal contribution)
2026
Athina Kyriakou, Dennis Ulmer, Ivan Titov: Shared Doubt: Zero-shot Cross-Lingual Confidence Estimation for Language Models (EMNLP 2026) [pdf]
Ivo Brink, Alexander Boer, Dennis Ulmer: Probing for Knowledge Attribution in Large Language Models (EMNLP 2026 BlackboxNLP Workshop) [pdf]
Yuxi Xia, Dennis Ulmer, Terra Blevins, Yihong Liu, Hinrich Schütze, Benjamin Roth: Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations (EMNLP 2026 UncertaiNLP Workshop) [pdf]
Sabine Weber, Angelina Wang, Ankush Gupta, Arjun Subramonian, Dennis Ulmer, Eshaan Tanwar, Geetanjali Aich, Hannah Devinney, Jacob Hobbs, Jennifer Mickel, Joshua Tint, Mae Sosto, Ray Groshan, Simone Astarita, Vagrant Gautam, Verena Blaschke, William Agnew, Wilson Y. Lee, Yanan Long: Queer NLP: A Critical Survey on Literature Gaps, Biases and Trends (Transactions of the Association for Computational Linguistics) [pdf]
Dennis Ulmer, Alexandra Lorson, Ivan Titov, Christian Hardmeier: Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models Is Missing (Transactions of the Association for Computational Linguistics) [pdf]
2024
Dennis Ulmer: On Uncertainty In Natural Language Processing (Doctoral thesis) [pdf]
Dennis Ulmer, Martin Gubri, Sangdoo Yun, Hwaran Lee, Seong Joon Oh: Calibrating Large Language Models Using Their Generations Only (ACL 2024) [pdf]
Martin Gubri, Dennis Ulmer, Sangdoo Yun, Hwaran Lee, Seong Joon Oh: TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification (Findings of ACL 2024) [pdf]
Dennis Ulmer, Elman Mansimov, Kaixiang Lin, Justin Sun, Xibin Gao, Yi Zhang: "Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk" (Findings of ACL 2024) [pdf]
2023
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, Christos Christodoulopoulos, Karim Lasri, Naomi Saphra, Arabella Sinclair, Dennis Ulmer, Florian Schottmann, Khuyagbaatar Batsuren, Kaiser Sun, Koustuv Sinha, Leila Khalatbari, Rita Frieske, Ryan Cotterell, Zhijing Jin: "State-of-the-art Generalisation Research in NLP: A Taxonomy and Review" (Nature Machine Intelligence) [pdf]
Joris Baan*, Nico Daheim*, Evgenia Ilia*, Dennis Ulmer*, Haau-Sing Li, Raquel Fernández, Barbara Plank, Rico Sennrich, Chrysoula Zerva, Wilker Aziz: "Uncertainty in Natural Language Generation: From Theory to Applications" (Preprint) [pdf]
Dennis Ulmer, Christian Hardmeier, Jes Frellsen: "Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation" (Transactions on Machine Learning Research) [pdf]
2022
Dennis Ulmer, Jes Frellsen, Christian Hardmeier: "Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity" (Findings of EMNLP 2022) [pdf] [model code]
Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank: "Experimental Standards for Deep Learning in Natural Language Processing Research" (Findings of EMNLP 2022) [pdf] [resource repo]
Dennis Ulmer, Christian Hardmeier, Jes Frellsen: "deep-significance: Easy and Meaningful Significance Testing in the Age of Neural Networks" (Machine Learning Evaluation Standards at ICLR 2022) [pdf] [code]
Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Christian Hardmeier, Barbara Plank: "Experimental Standards for Deep Learning Research: A Natural Language Processing Perspective" (selected as outstanding paper at Machine Learning Evaluation Standards at ICLR 2022) [pdf] [repo]
2021
Dennis Ulmer, Giovanni Cinà: "Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection" (UAI 2021) [pdf]
2020
Dennis Ulmer, Lotta Meijerink, Giovanni Cinà: "Trust issues: Uncertainty estimation does not enable reliable OOD detection on medical tabular data" (NeurIPS 2020 ML4H Workshop) [pdf]
2019
Joris Baan*, Jana Leible*, Mitja Nikolaus*, David Rau*, Dennis Ulmer*, Tim Baumgärtner, Dieuwke Hupkes, Elia Bruni: "On the realization of compositionality in neural networks" (ACL 2019 BlackboxNLP Workshop) [pdf]
Dennis Ulmer, Dieuwke Hupkes, Elia Bruni: "Assessing incrementality in sequence-to-sequence models" (ACL 2019 Repl4NLP Workshop) [pdf]
Dennis Ulmer: "Recoding latent sentence representations - Dynamic gradient-based activation modification in RNNs" (Master Thesis) [pdf]