70 citations · 145 across the 19 of their papers we have counts for
5 papers · 1 filter
On Uncertainty In Natural Language Processing
Dennis Ulmer
The last decade in deep learning has brought on increasingly capable systems that are deployed on a wide variety of applications. In natural language processing, the field has been…
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…
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Martin Gubri, Dennis Ulmer, Hwaran Lee +2
Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them. Assessing the compliance of the released LLMs is crucial,…
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…
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk
Dennis Ulmer, Elman Mansimov, Kaixiang Lin +3
Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be challenging. Instructing tuning, i.e. tuning models o…