14 citations · 17 across the 13 of their papers we have counts for
12 papers · 1 filter
CRADLE Bench: A Clinician-Annotated Benchmark for Multi-Faceted Mental Health Crisis and Safety Risk Detection
Grace Byun, Rebecca Lipschutz, Sean T. Minton +2
Detecting mental health crisis situations such as suicide ideation, rape, domestic violence, child abuse, and sexual harassment is a critical yet underexplored challenge for langua…
Measuring Sycophancy of Language Models in Multi-turn Dialogues
Jiseung Hong, Grace Byun, Seungone Kim +2
Large Language Models (LLMs) are expected to provide helpful and harmless responses, yet they often exhibit sycophancy--conforming to user beliefs regardless of factual accuracy or…
D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model
Grace Byun, Jinho D. Choi
Evaluating generative models with open-ended generation is challenging due to inconsistencies in response formats. Multiple-choice (MC) evaluation mitigates this issue, but generat…
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…
ManWav: The First Manchu ASR Model
Jean Seo, Minha Kang, Sungjoo Byun +1
This study addresses the widening gap in Automatic Speech Recognition (ASR) research between high resource and extremely low resource languages, with a particular focus on Manchu,…
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…
Mergen: The First Manchu-Korean Machine Translation Model Trained on Augmented Data
Jean Seo, Sungjoo Byun, Minha Kang +1
The Manchu language, with its roots in the historical Manchurian region of Northeast China, is now facing a critical threat of extinction, as there are very few speakers left. In o…