7 citations · 10 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models
Hyeonwoo Kim, Gyoungjin Gim, Yungi Kim +4
This study presents a novel learning approach designed to enhance both mathematical reasoning and problem-solving abilities of Large Language Models (LLMs). We focus on integrating…
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…
Lightweight Boosting Models for User Response Prediction Using Adversarial Validation
Hyeonwoo Kim, Wonsung Lee
The ACM RecSys Challenge 2023, organized by ShareChat, aims to predict the probability of the app being installed. This paper describes the lightweight solution to this challenge.…