1 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
CoME: An Unlearning-based Approach to Conflict-free Model Editing
Dahyun Jung, Jaehyung Seo, Jaewook Lee +2
Large language models (LLMs) often retain outdated or incorrect information from pre-training, which undermines their reliability. While model editing methods have been developed t…
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models
Suhyune Son, Chanjun Park, Jungseob Lee +5
As pre-trained language models become more resource-demanding, the inequality between resource-rich languages such as English and resource-scarce languages is worsening. This can b…
A New Tool for Efficiently Generating Quality Estimation Datasets
Sugyeong Eo, Chanjun Park, Jaehyung Seo +2
Building of data for quality estimation (QE) training is expensive and requires significant human labor. In this study, we focus on a data-centric approach while performing QE, and…
Automatic Knowledge Augmentation for Generative Commonsense Reasoning
Jaehyung Seo, Chanjun Park, Sugyeong Eo +2
Generative commonsense reasoning is the capability of a language model to generate a sentence with a given concept-set that is based on commonsense knowledge. However, generative l…
How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus
Chanjun Park, Seolhwa Lee, Hyeonseok Moon +3
This paper proposes a tool for efficiently constructing high-quality parallel corpora with minimizing human labor and making this tool publicly available. Our proposed construction…
Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC
Chanjun Park, Midan Shim, Sugyeong Eo +4
Machine translation (MT) system aims to translate source language into target language. Recent studies on MT systems mainly focus on neural machine translation (NMT). One factor th…