1 citations · 2 across the 14 of their papers we have counts for
4 papers · 1 filter
Learning to Hear Hesitation: Continual Learning for Disfluency-Aware ASR
Henri-Leon Kordt, Theresa Pekarek Rosin, Jae Hee Lee +1
Despite advances in large-scale Automatic Speech Recognition (ASR), disfluent speech remains challenging, as state-of-the-art systems are often optimized to omit disfluencies, lead…
The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
Jeremy Herbst, Stefan Wermter, Jae Hee Lee
Mixture-of-Experts (MoE) architectures have become the dominant choice for scaling Large Language Models (LLMs), activating only a subset of parameters per token. While MoE archite…
Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities
Hans Hergen Lehmann, Jae Hee Lee, Steven Schockaert +1
Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains…
Visually Grounded Continual Language Learning with Selective Specialization
Kyra Ahrens, Lennart Bengtson, Jae Hee Lee +1
A desirable trait of an artificial agent acting in the visual world is to continually learn a sequence of language-informed tasks while striking a balance between sufficiently spec…