1 citations · 2 across the 12 of their papers we have counts for
12 papers
RCScore: Quantifying Response Consistency in Large Language Models
Dongjun Jang, Youngchae Ahn, Hyopil Shin
Current LLM evaluations often rely on a single instruction template, overlooking models' sensitivity to instruction style-a critical aspect for real-world deployments. We present R…
P-CoT: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in LLMs
Dongjun Jang, Youngchae Ahn, Hyopil Shin
This study explores the potential of phonological reasoning within text-based large language models (LLMs). Utilizing the PhonologyBench benchmark, we assess tasks like rhyme word…
KoBALT: Korean Benchmark For Advanced Linguistic Tasks
Hyopil Shin, Sangah Lee, Dongjun Jang +9
We introduce KoBALT (Korean Benchmark for Advanced Linguistic Tasks), a comprehensive linguistically-motivated benchmark comprising 700 multiple-choice questions spanning 24 phenom…
MoFE: Mixture of Frozen Experts Architecture
Jean Seo, Jaeyoon Kim, Hyopil Shin
We propose the Mixture of Frozen Experts (MoFE) architecture, which integrates Parameter-efficient Fine-tuning (PEFT) and the Mixture of Experts (MoE) architecture to enhance both…
How does a Language-Specific Tokenizer affect LLMs?
Jean Seo, Jaeyoon Kim, SungJoo Byun +1
The necessity of language-specific tokenizers intuitively appears crucial for effective natural language processing, yet empirical analyses on their significance and underlying rea…
DAHL: Domain-specific Automated Hallucination Evaluation of Long-Form Text through a Benchmark Dataset in Biomedicine
Jean Seo, Jongwon Lim, Dongjun Jang +1
We introduce DAHL, a benchmark dataset and automated evaluation system designed to assess hallucination in long-form text generation, specifically within the biomedical domain. Our…
A Study on How Attention Scores in the BERT Model are Aware of Lexical Categories in Syntactic and Semantic Tasks on the GLUE Benchmark
Dongjun Jang, Sungjoo Byun, Hyopil Shin
This study examines whether the attention scores between tokens in the BERT model significantly vary based on lexical categories during the fine-tuning process for downstream tasks…
KIT-19: A Comprehensive Korean Instruction Toolkit on 19 Tasks for Fine-Tuning Korean Large Language Models
Dongjun Jang, Sungjoo Byun, Hyemi Jo +1
Instruction Tuning on Large Language Models is an essential process for model to function well and achieve high performance in specific tasks. Accordingly, in mainstream languages…
Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition
Sungjoo Byun, Jiseung Hong, Sumin Park +5
Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP). Yet, there has not been an open-source medical NER dataset specifically for the Ko…
CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean
Dongjun Jang, Jean Seo, Sungjoo Byun +3
This paper explores the challenges posed by aspect-based sentiment classification (ABSC) within pretrained language models (PLMs), with a particular focus on contextualization and…
Automatic Construction of a Korean Toxic Instruction Dataset for Ethical Tuning of Large Language Models
Sungjoo Byun, Dongjun Jang, Hyemi Jo +1
Caution: this paper may include material that could be offensive or distressing. The advent of Large Language Models (LLMs) necessitates the development of training approaches that…
DaG LLM ver 1.0: Pioneering Instruction-Tuned Language Modeling for Korean NLP
Dongjun Jang, Sangah Lee, Sungjoo Byun +8
This paper presents the DaG LLM (David and Goliath Large Language Model), a language model specialized for Korean and fine-tuned through Instruction Tuning across 41 tasks within 1…