Home  | News

15.04.2026

Teaser image to Michael A. Hedderich Featured in “KI Pro” Podcast

Michael A. Hedderich Featured in “KI Pro” Podcast

Discussing Trust, Limitations, and Evaluation of LLMs From an Industry Perspective

MCML Junior Research Group Leader Michael A. Hedderich was recently interviewed by heise for their industry-focused “KI Pro” podcast. In the episode, he discusses key questions around large language models (LLMs), including how we can better understand their limitations and improve trust in their outputs.

The conversation offered valuable insights into how these topics are perceived from an industry perspective, particularly among practitioners and small and medium-sized enterprises (SMEs). It also highlighted the growing importance of reliable evaluation methods for LLMs in real-world applications.

The discussion builds on recent research conducted at MCML, including a Spotlight project on new approaches to evaluating LLM outputs, as well as a study on the reliability of knowledge probes for LLMs led by Raoyuan Zhang.

The podcast episode is available in German via heise’s “KI Pro” format.

#media #research #hedderich

Related

Tiny logo
Link to MCML at COLT 2026

26.06.2026

MCML at COLT 2026

MCML researchers are represented with 1 paper at COLT 2026.

Read more
Tiny logo
Link to MCML at WWW 2026

26.06.2026

MCML at WWW 2026

MCML researchers are represented with 1 paper at WWW 2026.

Read more
Link to FIFA World Cup: How well can AI predict sports results?

19.06.2026

FIFA World Cup: How Well Can AI Predict Sports Results?

LMU researchers are putting different large language models head-to-head to find out which one delivers the most accurate predictions.

Read more
Tiny logo
Link to MCML at DIS 2026

12.06.2026

MCML at DIS 2026

MCML researchers are represented with 1 paper at DIS 2026.

Read more
Link to MCML PI Tom Sterkenburg Receives 2026 Simon Award

12.06.2026

MCML PI Tom Sterkenburg Receives 2026 Simon Award

Tom Sterkenburg receives the 2026 Simon Award for research at the intersection of computing, philosophy, and machine learning.

Read more
Back to Top