Papers by Hyeonwoo Kim
Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark (2024.acl-long)
Copied to clipboard
Chanjun Park, Hyeonwoo Kim, Dahyun Kim, SeongHwan Cho, Sanghoon Kim, Sukyung Lee, Yungi Kim, Hwalsuk Lee
| Challenge: | Existing benchmarks for evaluating Large Language Models are limited to the English language. |
| Approach: | They introduce the Open Ko-LLM Leaderboard and Ko-H5 Benchmark as tools for evaluating Large Language Models in Korean using private test sets. |
| Outcome: | The proposed evaluation framework is well integrated in the Korean LLM community. |
Understanding LLM Development Through Longitudinal Study: Insights from the Open Ko-LLM Leaderboard (2025.naacl-industry)
Copied to clipboard
| Challenge: | Existing studies on the Open Ko-LLM Leaderboard have been limited to five months . this limited analysis of the Open LLM Leaderboard provides a more comprehensive understanding of the progress in developing large language models. |
| Approach: | They conduct a longitudinal study over eleven months to address limitations of previous studies . they analyze 1,769 models over this period to provide a more comprehensive understanding . |
| Outcome: | The study extends observation period of the Open Ko-LLM Leaderboard to eleven months . primary questions are: What are the specific challenges in improving LLM performance? |
Open Ko-LLM Leaderboard2: Bridging Foundational and Practical Evaluation for Korean LLMs (2025.naacl-industry)
Copied to clipboard
| Challenge: | Open Ko-LLM Leaderboard has been instrumental in benchmarking Korean Large Language Models . however, the leaderboard has faced significant limitations over time due to its academic nature . |
| Approach: | They propose an improved version of the Open Ko-LLM Leaderboard to improve benchmarking . original benchmarks replaced with new tasks that align with real-world capabilities . four new native Korean benchmarks are introduced to better reflect distinct characteristics of Korean language . |
| Outcome: | The proposed framework improves the Open Ko-LLM Leaderboard2 benchmark suite. |
sDPO: Don’t Use Your Data All at Once (2025.coling-industry)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly requiring precision and accuracy in alignment tuning. |
| Approach: | They propose a stepwise DPO technique that partitions available preference datasets incrementally rather than utilizing entire dataset simultaneously. |
| Outcome: | The proposed technique improves the accuracy of reference models and the overall performance of the final model. |
SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Existing approaches to enhance mathematical reasoning and problem-solving abilities of Large Language Models (LLMs) despite their remarkable performance across domains, a notable challenge persists in the realm of mathematical reasoning. |
| Approach: | They propose a sequential learning approach that integrates the Chain-of-Thought and the Program-ofThough. |
| Outcome: | The proposed approach achieves state-of-the-art (SOTA) performance by integrating CoT and PoT learning. |