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cs.CL2024
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.CL2024
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.CL2024
MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs
Yerin Hwang, Yongil Kim, Yunah Jang +3
Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability…