3 papers
cs.CL2025
Are LLM-Judges Robust to Expressions of Uncertainty? Investigating the effect of Epistemic Markers on LLM-based Evaluation
Dongryeol Lee, Yerin Hwang, Yongil Kim +2
In line with the principle of honesty, there has been a growing effort to train large language models (LLMs) to generate outputs containing epistemic markers. However, evaluation i…
cs.CL2025
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models
Jahyun Koo, Yerin Hwang, Yongil Kim +3
Despite the success of Large Language Models (LLMs), they still face challenges related to high inference costs and memory requirements. To address these issues, Knowledge Distilla…
cs.CL2025
LLMs can be easily Confused by Instructional Distractions
Yerin Hwang, Yongil Kim, Jahyun Koo +3
Despite the fact that large language models (LLMs) show exceptional skill in instruction following tasks, this strength can turn into a vulnerability when the models are required t…