61 citations · 193 across the 17 of their papers we have counts for
17 papers
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Gihun Lee, Minchan Jeong, Yujin Kim +4
While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents furthe…
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning
Seongyoon Kim, Gihun Lee, Jaehoon Oh +1
Federated Learning (FL) is a collaborative method for training models while preserving data privacy in decentralized settings. However, FL encounters challenges related to data het…
Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval
Seongha Eom, Namgyu Ho, Jaehoon Oh +1
Contrastive language-image pre-training (CLIP) has demonstrated remarkable zero-shot classification ability, namely image classification using novel text labels. Existing works hav…
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
Gihun Lee, Minchan Jeong, Sangmook Kim +2
Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers…
Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks
Jaehoon Oh, Jongwoo Ko, Se-Young Yun
Translation has played a crucial role in improving the performance on multilingual tasks: (1) to generate the target language data from the source language data for training and (2…
Demystifying the Base and Novel Performances for Few-shot Class-incremental Learning
Jaehoon Oh, Se-Young Yun
Few-shot class-incremental learning (FSCIL) has addressed challenging real-world scenarios where unseen novel classes continually arrive with few samples. In these scenarios, it is…