Papers by YoungHyun Cho
QEFT: Quantization for Efficient Fine-Tuning of LLMs (2024.findings-emnlp)
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| Challenge: | Existing methods to optimize inference and fine-tuning for large language models have failed to improve all aspects of the process. |
| Approach: | They propose a new technique that accelerates both inference and fine-tuning while using fewer resources. |
| Outcome: | The proposed technique accelerates both inference and fine-tuning while using fewer resources. |
SEAL: Scaling to Emphasize Attention for Long-Context Retrieval (2025.acl-long)
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| Challenge: | Recent studies show that advanced LLMs suffer from degradation when processing longer context data. |
| Approach: | They propose a learning-based mechanism that leverages generated data to emphasize attention heads for long-context retrieval. |
| Outcome: | The proposed approach improves retrieval performance over long contexts while maintaining high reliability. |