7 citations · 11 across the 9 of their papers we have counts for
9 papers
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…
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…
Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark
Chanjun Park, Hyeonwoo Kim, Dahyun Kim +5
This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as vital tools for evaluating Large Language Models (LLMs) in Korean. Incorporating private test sets whil…
Evalverse: Unified and Accessible Library for Large Language Model Evaluation
Jihoo Kim, Wonho Song, Dahyun Kim +3
This paper introduces Evalverse, a novel library that streamlines the evaluation of Large Language Models (LLMs) by unifying disparate evaluation tools into a single, user-friendly…
Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism
Chanjun Park, Minsoo Khang, Dahyun Kim
This paper delves into the contrasting roles of data within academic and industrial spheres, highlighting the divergence between Data-Centric AI and Model-Agnostic AI approaches. W…
Dataverse: Open-Source ETL (Extract, Transform, Load) Pipeline for Large Language Models
Hyunbyung Park, Sukyung Lee, Gyoungjin Gim +3
To address the challenges associated with data processing at scale, we propose Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline for large language models (LLM…
sDPO: Don't Use Your Data All at Once
Dahyun Kim, Yungi Kim, Wonho Song +4
As development of large language models (LLM) progresses, aligning them with human preferences has become increasingly important. We propose stepwise DPO (sDPO), an extension of th…
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Dahyun Kim, Chanjun Park, Sanghoon Kim +15
We introduce SOLAR 10.7B, a large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. Inspired…