Measuring Doubt in Systems That Have None: Uncertainty Quantification for LLMs

When a language model is wrong, it rarely sounds like it. Bridging the gap between statistical rigour and the fluid, high-dimensional outputs of modern AI is one of the central challenges for anyone deploying these systems where errors have real consequences.
We are excited to welcome Lynton Ardizzone, PhD in machine learning from Heidelberg University and independent ML/AI consultant, to our joint heidelberg.ai / NCT Data Science Seminar on 24th June at 5 PM.
In this in-person event, he will present a tour of uncertainty quantification for large language models, examining why classical UQ assumptions break down for these systems and what state-of-the-art approaches are doing about it.
We look forward to your participation in this seminar, where you will come away with a clearer sense of how far current methods get us, and what it would actually mean for a language model to know what it doesn't know.

For more info go to:
https://heidelberg.ai/2026/06/24/ardizzone.html

After the event, we will also upload a video recording to our YouTube channel:
https://www.youtube.com/channel/UCfHWBneOsb7SfOxJepnMQKA