1 citations · 1 across the 10 of their papers we have counts for
10 papers
LCIRC: A Recurrent Compression Approach for Efficient Long-form Context and Query Dependent Modeling in LLMs
Sumin An, Junyoung Sung, Wonpyo Park +2
While large language models (LLMs) excel in generating coherent and contextually rich outputs, their capacity to efficiently handle long-form contexts is limited by fixed-length po…
LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models
Yungi Kim, Hyunsoo Ha, Seonghoon Yang +3
Creating high-quality, large-scale datasets for large language models (LLMs) often relies on resource-intensive, GPU-accelerated models for quality filtering, making the process ti…
Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models
Dahyun Kim, Sukyung Lee, Yungi Kim +2
The rapid advancement of large language models (LLMs) has highlighted the need for robust evaluation frameworks that assess their core capabilities, such as reasoning, knowledge, a…
InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets
Yungi Kim, Chanjun Park
It is challenging to generate high-quality instruction datasets for non-English languages due to tail phenomena, which limit performance on less frequently observed data. To mitiga…
Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching
Seoyeon Kim, Huiseo Kim, Chanjun Park +2
Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. The limited resource for low-r…
Open Ko-LLM Leaderboard2: Bridging Foundational and Practical Evaluation for Korean LLMs
Hyeonwoo Kim, Dahyun Kim, Jihoo Kim +3
The Open Ko-LLM Leaderboard has been instrumental in benchmarking Korean Large Language Models (LLMs), yet it has certain limitations. Notably, the disconnect between quantitative…
1 Trillion Token (1TT) Platform: A Novel Framework for Efficient Data Sharing and Compensation in Large Language Models
Chanjun Park, Hyunsoo Ha, Jihoo Kim +4
In this paper, we propose the 1 Trillion Token Platform (1TT Platform), a novel framework designed to facilitate efficient data sharing with a transparent and equitable profit-shar…
Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora
Yungi Kim, Hyunsoo Ha, Sukyung Lee +3
With the increasing demand for substantial amounts of high-quality data to train large language models (LLMs), efficiently filtering large web corpora has become a critical challen…
MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning
Seungbeom Hu, ChanJun Park, Andrew Ferraiuolo +4
Determining the optimal size of a neural network is critical, as it directly impacts runtime performance and memory usage. Pruning is a well-established model compression technique…
Understanding LLM Development Through Longitudinal Study: Insights from the Open Ko-LLM Leaderboard
Chanjun Park, Hyeonwoo Kim
This paper conducts a longitudinal study over eleven months to address the limitations of prior research on the Open Ko-LLM Leaderboard, which have relied on empirical studies with…