1 citations · 1 across the 5 of their papers we have counts for
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MIDUS: Memory-Infused Depth Up-Scaling
Taero Kim, Hoyoon Byun, Youngjun Choi +2
Expanding pre-trained language models offers a practical way to increase capacity without training larger models from scratch. Depth Up-Scaling (DUS) does so by duplicating Transfo…
Adaptive Task Vectors for Large Language Models
Joonseong Kang, Soojeong Lee, Subeen Park +5
In-Context Learning (ICL) enables Large Language Models (LLMs) to perform tasks without parameter updates by conditioning on a few demonstrations provided in the prompt. Despite it…
Sufficient Invariant Learning for Distribution Shift
Taero Kim, Subeen Park, Sungjun Lim +3
Learning robust models under distribution shifts between training and test datasets is a fundamental challenge in machine learning. While learning invariant features across environ…
Learning Fair Representation via Distributional Contrastive Disentanglement
Changdae Oh, Heeji Won, Junhyuk So +4
Learning fair representation is crucial for achieving fairness or debiasing sensitive information. Most existing works rely on adversarial representation learning to inject some in…