1 citations · 2 across the 12 of their papers we have counts for
5 papers · 1 filter
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…