Yonghyun Jun

When privacy rules forbid raw audio access, general LLMs falter on sparse ASR transcription mistakes—making two-stage, detector-gated span correction the far more effective paradigm.

LLMs can be steered to respect instruction hierarchies in multi-turn dialogues without the need for costly fine-tuning, revealing a novel approach to mitigating role-influence inversion.

Code-switching in multilingual models reveals a surprising grammar-frame effect that can significantly degrade performance, but a new intervention can effectively mitigate these issues.