3 papers
cs.AI2026
PULSE: Identifying Demonstration-Utility Features with Sparse Autoencoders
Chenduo Hao, Chuanbao Gao, Pinjun Zeng +4
In-context learning is highly sensitive to demonstration choice, yet most methods select demonstrations using external query-demonstration similarity. Such criteria can miss model-…
cs.LG2025
Mimic In-Context Learning for Multimodal Tasks
Yuchu Jiang, Jiale Fu, Chenduo Hao +4
Recently, In-context Learning (ICL) has become a significant inference paradigm in Large Multimodal Models (LMMs), utilizing a few in-context demonstrations (ICDs) to prompt LMMs f…
cs.CL2024
LIVE: Learnable In-Context Vector for Visual Question Answering
Yingzhe Peng, Chenduo Hao, Xu Yang +3
As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefi…