collaborators

5 papers

cs.CL2026

Shared Doubt: Zero-Shot Cross-Lingual Confidence Estimation for Language Models

Athina Kyriakou, Dennis Ulmer, Ivan Titov

Confidence estimation (CE), i.e., quantifying the reliability of a model's prediction, has attracted great interest in the context of large language models (LLMs). However, most st…

cs.CY2026

Queer NLP: A Critical Survey on Literature Gaps, Biases and Trends

Sabine Weber, Angelina Wang, Ankush Gupta +16

Natural language processing (NLP) technologies are rapidly reshaping how language is created, processed, and interpreted by humans. With current and potential applications in hirin…

cs.CL20264 cited

Probing for Knowledge Attribution in Large Language Models

Ivo Brink, Alexander Boer, Dennis Ulmer

Large language model (LLM) hallucinations, meaning fluent but factually incorrect generations, fall into two types: faithfulness violations, where the model misuses provided contex…

cs.CL2026

Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations

Yuxi Xia, Dennis Ulmer, Terra Blevins +3

Confidence estimation (CE) indicates how reliable the answers of large language models are and impacts user trust and decision-making. Existing evaluations mainly concern the align…

cs.CL2026

Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing

Dennis Ulmer, Alexandra Lorson, Ivan Titov +1

Human users increasingly communicate with large language models (LLMs), but LLMs suffer from frequent overconfidence in their output, even when its accuracy is questionable, which…