1 citations · 1 across the 4 of their papers we have counts for
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KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms?
SungHo Kim, Eojin Jeon, SangKeun Lee
Korean neologisms arise through diverse mechanisms, a demanding test of language models beyond lexical memorization. To enable systematic evaluation, we introduce NeoTrace, an auto…
PICTURE: Enhancing Theory-of-Mind in Large Language Models by Revealing, Not Hiding, Characters' Lack of Knowledge
Eojin Jeon, SangKeun Lee
Simulating human-like Theory of Mind (ToM) has been a longstanding problem in natural language processing (NLP). To address this, existing works introduce a reasoning step of event…
Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learning
Nayeon Kim, Eojin Jeon, Jun-Hyung Park +1
In this study, we introduce KOPL, a novel framework for handling Korean OOV words with Phoneme representation Learning. Our work is based on the linguistic property of Korean as a…
Improving Bias Mitigation through Bias Experts in Natural Language Understanding
Eojin Jeon, Mingyu Lee, Juhyeong Park +3
Biases in the dataset often enable the model to achieve high performance on in-distribution data, while poorly performing on out-of-distribution data. To mitigate the detrimental e…