Papers by Hyeon Hwang
Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models (2025.acl-long)
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| Challenge: | Quantization is a practical solution for deploying Large Language Models in resource-constrained environments. |
| Approach: | They propose an outlier-safe pre-training approach that prevents outlier formation . they validate a 1.4B-parameter model on 1 trillion tokens with no outliers . |
| Outcome: | The proposed model achieves a 35.7 average score on 1 trillion tokens with 2% training overhead. |
Rationale-Guided Retrieval Augmented Generation for Medical Question Answering (2025.naacl-long)
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Jiwoong Sohn, Yein Park, Chanwoong Yoon, Sihyeon Park, Hyeon Hwang, Mujeen Sung, Hyunjae Kim, Jaewoo Kang
| Challenge: | Large language models (LLMs) struggle with hallucinations and outdated knowledge. |
| Approach: | They propose a retrieval-augmented generation framework for enhancing the reliability of RAG in biomedical contexts. |
| Outcome: | The proposed framework outperforms the previous best medical RAG model by up to 5.6% across three medical question-answering benchmarks. |
CompAct: Compressing Retrieved Documents Actively for Question Answering (2024.emnlp-main)
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| Challenge: | Existing methods to condense extensive documents with no loss of information are difficult to implement in real-world scenarios. |
| Approach: | They propose a framework that employs an active strategy to condense extensive documents without losing key information. |
| Outcome: | The proposed framework improves performance and compression rate on multi-hop question-answering benchmarks. |
Assessing LLM Reasoning Steps via Principal Knowledge Grounding (2025.findings-emnlp)
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Hyeon Hwang, Yewon Cho, Chanwoong Yoon, Yein Park, Minju Song, Kyungjae Lee, Gangwoo Kim, Jaewoo Kang
| Challenge: | Step-by-step reasoning has become a standard approach for large language models to tackle complex tasks. |
| Approach: | They propose a framework that assesses the knowledge grounding of intermediate reasoning by using a large-scale repository of atomic knowledge essential for reasoning. |
| Outcome: | The evaluation suite identifies missing or misapplied knowledge elements and provides crucial insights for uncovering fundamental reasoning deficiencies in LLMs. |