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YoungHyun Cho

2 accepted papers

2025

SEAL: Scaling to Emphasize Attention for Long-Context Retrieval

ACL 2025long

While many advanced LLMs are designed to handle long sequence data, we can still observe notable quality degradation even within the sequence limit. In this work, we introduce a novel approach called Scaling to Emphasize Attention for Long-context retrieval (SEAL), which enhances the retrieval perfo…

2024

QEFT: Quantization for Efficient Fine-Tuning of LLMs

EMNLP 2024finding

With the rapid growth in the use of fine-tuning for large language models (LLMs), optimizing fine-tuning while keeping inference efficient has become highly important. However, this is a challenging task as it requires improvements in all aspects, including inference speed, fine-tuning speed, memory…