5 citations · 9 across the 7 of their papers we have counts for
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In-Context Learning with Hypothesis-Class Guidance
Ziqian Lin, Shubham Kumar Bharti, Kangwook Lee
Recent research has investigated the underlying mechanisms of in-context learning (ICL) both theoretically and empirically, often using data generated from simple function classes.…
Parameter-Efficient Fine-Tuning of State Space Models
Kevin Galim, Wonjun Kang, Yuchen Zeng +2
Deep State Space Models (SSMs), such as Mamba (Gu & Dao, 2024), have become powerful tools for language modeling, offering high performance and linear scalability with sequence len…
Dual Operating Modes of In-Context Learning
Ziqian Lin, Kangwook Lee
In-context learning (ICL) exhibits dual operating modes: task learning, i.e., acquiring a new skill from in-context samples, and task retrieval, i.e., locating and activating a rel…
Can MLLMs Perform Text-to-Image In-Context Learning?
Yuchen Zeng, Wonjun Kang, Yicong Chen +2
The evolution from Large Language Models (LLMs) to Multimodal Large Language Models (MLLMs) has spurred research into extending In-Context Learning (ICL) to its multimodal counterp…
The Expressive Power of Low-Rank Adaptation
Yuchen Zeng, Kangwook Lee
Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that leverages low-rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre…