no code implementations • 19 Mar 2024 • Dojun Park, Jiwoo Lee, Hyeyun Jeong, Seohyun Park, Sungeun Lee
The current evaluation of Large Language Models (LLMs) predominantly relies on benchmarks focusing on their embedded knowledge by testing through multiple-choice questions (MCQs), a format inherently suited for automated evaluation.
no code implementations • 19 Mar 2024 • Dojun Park, Sebastian Padó
Almost all frameworks for the manual or automatic evaluation of machine translation characterize the quality of an MT output with a single number.
no code implementations • 24 Nov 2022 • Dojun Park, Seohyun Park
In this study, we experimented to examine the effect of adding the most frequent n phoneme bigrams to the basic vocabulary on the German phoneme recognition model using the text-to-phoneme data augmentation strategy.
no code implementations • 29 May 2021 • Dojun Park, Youngjin Jang, Harksoo Kim
Natural Language Generation (NLG) refers to the operation of expressing the calculation results of a system in human language.
1 code implementation • 29 May 2021 • Dojun Park, Youngjin Jang, Harksoo Kim
This work was conducted to find out how tokenization methods affect the training results of machine translation models.