4 papers · 1 filter
PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality
Byeongho Yu, Changhun Lee, Jungyu Jin +1
To mitigate the hallucination problem in large language models, DoLa exploits early exit logits from the same model as a contrastive prior. However, we found that these early exit…
SEAL: Scaling to Emphasize Attention for Long-Context Retrieval
Changhun Lee, Minsang Seok, Jun-gyu Jin +2
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 no…
QEFT: Quantization for Efficient Fine-Tuning of LLMs
Changhun Lee, Jun-gyu Jin, Younghyun Cho +1
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, thi…
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Changhun Lee, Jungyu Jin, Taesu Kim +2
Large language models (LLMs) with hundreds of billions of parameters require powerful server-grade GPUs for inference, limiting their practical deployment. To address this challeng…