19 citations · 48 across the 13 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
Calibrating Large Language Models Using Their Generations Only
Dennis Ulmer, Martin Gubri, Hwaran Lee +2
As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its p…
Non-Exchangeable Conformal Language Generation with Nearest Neighbors
Dennis Ulmer, Chrysoula Zerva, André F. T. Martins
Quantifying uncertainty in automatically generated text is important for letting humans check potential hallucinations and making systems more reliable. Conformal prediction is an…