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cs.CL2026
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…
cs.CL2026
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…
cs.CL2026
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…